{smcl}
{txt}{sf}{ul off}{.-}
      name:  {res}<unnamed>
       {txt}log:  {res}C:\Users\gk57526\Dropbox\Administration of UI Programs in the American States (Ji-Hyeun Hong)\Performance Management Project (Paper #3)\EMPIRICS\OUTPUT\Performance Management.APPENDIX B MODELS.04-10-2025.smcl
  {txt}log type:  {res}smcl
 {txt}opened on:  {res}10 Apr 2025, 18:32:58
{txt}
{com}. 
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. **** TABLE B1 -- MODELS B1-B3: "TASK COMPLEXITY, ORGANIZATIONAL ADAPTATION & PROGRAM ERROR RATES" APPENDIX B STATISTICAL ANALYSES [APRIL 2025]: ORGANIZATIONAL ADAPTATION EFFECTS ON PROGRAM PAYMENT ERROR RATES [TOTAL PROGRAM ERROR RATE] **** 
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. ** (MODEL B1; FIGURES B1A-B1C; MODEL B2: FIGURES B2A-B2C; MODEL B3: FIGURES B2D-B2F) **
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. *** MODELS PREDICTING VARIOPUS TYPE OF PROGRAM ERROR RATES BASED ON BAM SAMPLING RATES ***
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. *** MODEL 1: OVERALL ERROR RATE: (SAMPLE WEIGHTED) ***
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. *  [# overpayment errors / paid claims sample] + [# underpayment errors / paid claims sample] + [# erroneous denials / denied claims sample] + [# underpayment errors / denied claims sample] *
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. *** MODEL 2: ABSOLUTE TYPE I ERROR RATE ***
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. * [overpayment error rate / paid claims sample] *
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. *** MODEL 3: RELATIVE TYPE I ERROR RATE:  {c -(}TYPE I ERROR RATE /  [TYPE I ERROR RATE + TYPE II ERROR RATE]{c )-}      (SAMPLE WEIGHTED)  ***
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. *  {c -(}[overpayment error rate / paid claims sample]   /  [overpayment error rate / paid claims sample]   +  [underpayment error rate / paid claims sample]  +  [erroneous denial / denied claims sample]  +  [underpayment error / denied claims sample]{c )-}  *
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. *** APPENDIX B MODELS: ANALYZING SENSITIVITY OF MANUSCRIPT MODEL ESTIMATES -- INCLUSION OF ONLY STATE, YEAR, AND STATE ADOPTION YEAR COHORT*TREATMENT UNIT EFFECT INDICATORS AS COVARIATES ///
> ***                    --I.E., OMIT ADDITIONAL CONTROL COVARIATES ***
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. *** RETRIEVE MANUSCRIPT MODELS DATABASE [as of 04-10-2025] ***
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. use "C:\Users\gk57526\Dropbox\Administration of UI Programs in the American States (Ji-Hyeun Hong)\Performance Management Project (Paper #3)\EMPIRICS\DATA\Performance Management.MANUSCRIPT DATABASE.04-10-2025.dta", replace
{txt}
{com}. 
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. *** SET DATA TO PANEL STRUCTURE  ***
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. xtset stateid monthyear, monthly
{res}
{col 1}{txt:Panel variable: }{res:stateid}{txt: (unbalanced)}
{p 1 16 2}{txt:Time variable: }{res:monthyear}{txt:, }{res:{bind:2002m1}}{txt: to }{res:{bind:2022m9}}{p_end}
{txt}{col 10}Delta: {res}1 month
{txt}
{com}. 
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. *** TESTING H1 & H3: TOTAL/OVERALL PROGRAM E0RROR RATE ORGANIZATIONAL ADAPTATION HYPOTHESES  ***
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. *** ESTIMATE MODEL B1: TOTAL PROGRAM ERROR  RATE [MODEL 1 omitting ADDITIONAL COVARIATES: PROPORTION OF SAMPLE-WEIGHTED CASES OF TOTAL ERRORS VIA WEEKLY BAM SURVEY AGGREGATED TO MONTHLY OBSERVATIONS: [CONTROLS, PLUS STATE, YEAR, & YEAR-ADOPTION COHORT*TREATMENT UNIT EFFECTS] ***         (MODEL B1: FIGURES B1A-B1C) 
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. npregress series   totalerror_rat  itmod_monthcount  i.tot_interstate_cat  i.tot_diffoccupseek_cat  if itmod_adopt_state==1, asis(i.stateid i.year adoptcohort_2002_itadopt  adoptcohort_2004_itadopt  adoptcohort_2006_itadopt adoptcohort_2007_itadopt   adoptcohort_2009_itadopt  adoptcohort_2010_itadopt  adoptcohort_2013_itadopt  adoptcohort_2014_itadopt  adoptcohort_2015_itadopt  adoptcohort_2016_itadopt  adoptcohort_2017_itadopt  adoptcohort_2018_itadopt  adoptcohort_2020_itadopt  adoptcohort_2021_itadopt)  vce(bootstrap, seed(123) rep(1000))
{res}{txt}(running {bf:npregress} on estimation sample)
{res}
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done
{res}
{txt}Cubic B-spline estimation {col 44}Number of obs      =  {res}        7,000
{txt}Criterion: {res:cross validation}{col 44}Number of knots    =  {res}            1
{txt}{hline 25}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 26}{c |}   Observed{col 38}   Bootstrap{col 66}         Norm{col 79}al-based
{col 1}          totalerror_rat{col 26}{c |}     effect{col 38}   std. err.{col 50}      z{col 58}   P>|z|{col 66}     [95% con{col 79}f. interval]
{hline 25}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 8}itmod_monthcount {c |}{col 26}{res}{space 2}-.0008885{col 38}{space 2} .0002564{col 49}{space 1}   -3.47{col 58}{space 3}0.001{col 66}{space 4}-.0013911{col 79}{space 3} -.000386
{txt}{space 24} {c |}
{space 6}tot_interstate_cat {c |}
{space 22}1  {c |}{col 26}{res}{space 2}-.0015932{col 38}{space 2} .0034253{col 49}{space 1}   -0.47{col 58}{space 3}0.642{col 66}{space 4}-.0083066{col 79}{space 3} .0051203
{txt}{space 22}2  {c |}{col 26}{res}{space 2}-.0027814{col 38}{space 2} .0049948{col 49}{space 1}   -0.56{col 58}{space 3}0.578{col 66}{space 4} -.012571{col 79}{space 3} .0070083
{txt}{space 24} {c |}
{space 3}tot_diffoccupseek_cat {c |}
{space 22}1  {c |}{col 26}{res}{space 2} .0113366{col 38}{space 2} .0033664{col 49}{space 1}    3.37{col 58}{space 3}0.001{col 66}{space 4} .0047387{col 79}{space 3} .0179346
{txt}{space 22}2  {c |}{col 26}{res}{space 2} .0197044{col 38}{space 2} .0044706{col 49}{space 1}    4.41{col 58}{space 3}0.000{col 66}{space 4} .0109422{col 79}{space 3} .0284665
{txt}{space 24} {c |}
{space 17}stateid {c |}
{space 22}5  {c |}{col 26}{res}{space 2} .0748815{col 38}{space 2} .0092575{col 49}{space 1}    8.09{col 58}{space 3}0.000{col 66}{space 4} .0567373{col 79}{space 3} .0930258
{txt}{space 22}6  {c |}{col 26}{res}{space 2} .0717142{col 38}{space 2} .0077779{col 49}{space 1}    9.22{col 58}{space 3}0.000{col 66}{space 4} .0564698{col 79}{space 3} .0869587
{txt}{space 22}9  {c |}{col 26}{res}{space 2} .0365952{col 38}{space 2} .0089965{col 49}{space 1}    4.07{col 58}{space 3}0.000{col 66}{space 4} .0189623{col 79}{space 3} .0542281
{txt}{space 21}12  {c |}{col 26}{res}{space 2}-.0017836{col 38}{space 2} .0084129{col 49}{space 1}   -0.21{col 58}{space 3}0.832{col 66}{space 4}-.0182726{col 79}{space 3} .0147054
{txt}{space 21}13  {c |}{col 26}{res}{space 2} .1183442{col 38}{space 2} .0087263{col 49}{space 1}   13.56{col 58}{space 3}0.000{col 66}{space 4} .1012409{col 79}{space 3} .1354476
{txt}{space 21}14  {c |}{col 26}{res}{space 2}  .256719{col 38}{space 2} .0189257{col 49}{space 1}   13.56{col 58}{space 3}0.000{col 66}{space 4} .2196253{col 79}{space 3} .2938128
{txt}{space 21}18  {c |}{col 26}{res}{space 2} .2354621{col 38}{space 2} .0141194{col 49}{space 1}   16.68{col 58}{space 3}0.000{col 66}{space 4} .2077885{col 79}{space 3} .2631357
{txt}{space 21}19  {c |}{col 26}{res}{space 2} .1404928{col 38}{space 2} .0083188{col 49}{space 1}   16.89{col 58}{space 3}0.000{col 66}{space 4} .1241883{col 79}{space 3} .1567973
{txt}{space 21}20  {c |}{col 26}{res}{space 2} .0522015{col 38}{space 2} .0082079{col 49}{space 1}    6.36{col 58}{space 3}0.000{col 66}{space 4} .0361143{col 79}{space 3} .0682887
{txt}{space 21}21  {c |}{col 26}{res}{space 2}  .141181{col 38}{space 2} .0084744{col 49}{space 1}   16.66{col 58}{space 3}0.000{col 66}{space 4} .1245715{col 79}{space 3} .1577905
{txt}{space 21}22  {c |}{col 26}{res}{space 2} .0306789{col 38}{space 2}  .008376{col 49}{space 1}    3.66{col 58}{space 3}0.000{col 66}{space 4} .0142623{col 79}{space 3} .0470954
{txt}{space 21}23  {c |}{col 26}{res}{space 2} .0633136{col 38}{space 2} .0106356{col 49}{space 1}    5.95{col 58}{space 3}0.000{col 66}{space 4} .0424682{col 79}{space 3}  .084159
{txt}{space 21}24  {c |}{col 26}{res}{space 2}-.0751794{col 38}{space 2} .0095989{col 49}{space 1}   -7.83{col 58}{space 3}0.000{col 66}{space 4}-.0939928{col 79}{space 3} -.056366
{txt}{space 21}25  {c |}{col 26}{res}{space 2} .0134053{col 38}{space 2}   .00754{col 49}{space 1}    1.78{col 58}{space 3}0.075{col 66}{space 4}-.0013727{col 79}{space 3} .0281834
{txt}{space 21}27  {c |}{col 26}{res}{space 2} .1180713{col 38}{space 2}  .010447{col 49}{space 1}   11.30{col 58}{space 3}0.000{col 66}{space 4} .0975954{col 79}{space 3} .1385471
{txt}{space 21}28  {c |}{col 26}{res}{space 2} .0169795{col 38}{space 2} .0076331{col 49}{space 1}    2.22{col 58}{space 3}0.026{col 66}{space 4} .0020189{col 79}{space 3}   .03194
{txt}{space 21}29  {c |}{col 26}{res}{space 2} .1588758{col 38}{space 2} .0101529{col 49}{space 1}   15.65{col 58}{space 3}0.000{col 66}{space 4} .1389765{col 79}{space 3} .1787751
{txt}{space 21}31  {c |}{col 26}{res}{space 2} .0406127{col 38}{space 2} .0259403{col 49}{space 1}    1.57{col 58}{space 3}0.117{col 66}{space 4}-.0102292{col 79}{space 3} .0914547
{txt}{space 21}33  {c |}{col 26}{res}{space 2}  .023199{col 38}{space 2} .0070187{col 49}{space 1}    3.31{col 58}{space 3}0.001{col 66}{space 4} .0094426{col 79}{space 3} .0369555
{txt}{space 21}35  {c |}{col 26}{res}{space 2}   .13138{col 38}{space 2} .0171976{col 49}{space 1}    7.64{col 58}{space 3}0.000{col 66}{space 4} .0976734{col 79}{space 3} .1650866
{txt}{space 21}38  {c |}{col 26}{res}{space 2} .2225559{col 38}{space 2} .0140563{col 49}{space 1}   15.83{col 58}{space 3}0.000{col 66}{space 4}  .195006{col 79}{space 3} .2501058
{txt}{space 21}40  {c |}{col 26}{res}{space 2}   .03739{col 38}{space 2} .0085466{col 49}{space 1}    4.37{col 58}{space 3}0.000{col 66}{space 4}  .020639{col 79}{space 3} .0541411
{txt}{space 21}42  {c |}{col 26}{res}{space 2} .1575096{col 38}{space 2} .0079743{col 49}{space 1}   19.75{col 58}{space 3}0.000{col 66}{space 4} .1418803{col 79}{space 3} .1731389
{txt}{space 21}44  {c |}{col 26}{res}{space 2}  .019475{col 38}{space 2} .0099443{col 49}{space 1}    1.96{col 58}{space 3}0.050{col 66}{space 4}-.0000155{col 79}{space 3} .0389654
{txt}{space 21}46  {c |}{col 26}{res}{space 2} .0932346{col 38}{space 2} .0093018{col 49}{space 1}   10.02{col 58}{space 3}0.000{col 66}{space 4} .0750034{col 79}{space 3} .1114659
{txt}{space 21}47  {c |}{col 26}{res}{space 2} .0259369{col 38}{space 2}  .007259{col 49}{space 1}    3.57{col 58}{space 3}0.000{col 66}{space 4} .0117095{col 79}{space 3} .0401643
{txt}{space 21}50  {c |}{col 26}{res}{space 2} .0021688{col 38}{space 2} .0091573{col 49}{space 1}    0.24{col 58}{space 3}0.813{col 66}{space 4}-.0157792{col 79}{space 3} .0201168
{txt}{space 21}51  {c |}{col 26}{res}{space 2} .0966767{col 38}{space 2} .0096603{col 49}{space 1}   10.01{col 58}{space 3}0.000{col 66}{space 4} .0777429{col 79}{space 3} .1156104
{txt}{space 21}52  {c |}{col 26}{res}{space 2} .0690004{col 38}{space 2} .0094628{col 49}{space 1}    7.29{col 58}{space 3}0.000{col 66}{space 4} .0504537{col 79}{space 3} .0875471
{txt}{space 24} {c |}
{space 20}year {c |}
{space 19}2003  {c |}{col 26}{res}{space 2} .0028191{col 38}{space 2} .0069088{col 49}{space 1}    0.41{col 58}{space 3}0.683{col 66}{space 4}-.0107219{col 79}{space 3} .0163601
{txt}{space 19}2004  {c |}{col 26}{res}{space 2} .0087839{col 38}{space 2} .0070904{col 49}{space 1}    1.24{col 58}{space 3}0.215{col 66}{space 4} -.005113{col 79}{space 3} .0226809
{txt}{space 19}2005  {c |}{col 26}{res}{space 2}-.0034835{col 38}{space 2} .0071036{col 49}{space 1}   -0.49{col 58}{space 3}0.624{col 66}{space 4}-.0174063{col 79}{space 3} .0104394
{txt}{space 19}2006  {c |}{col 26}{res}{space 2} .0075014{col 38}{space 2}  .007085{col 49}{space 1}    1.06{col 58}{space 3}0.290{col 66}{space 4}-.0063849{col 79}{space 3} .0213877
{txt}{space 19}2007  {c |}{col 26}{res}{space 2} .0162398{col 38}{space 2} .0068637{col 49}{space 1}    2.37{col 58}{space 3}0.018{col 66}{space 4} .0027871{col 79}{space 3} .0296924
{txt}{space 19}2008  {c |}{col 26}{res}{space 2} .0199065{col 38}{space 2} .0071707{col 49}{space 1}    2.78{col 58}{space 3}0.006{col 66}{space 4} .0058521{col 79}{space 3} .0339609
{txt}{space 19}2009  {c |}{col 26}{res}{space 2} .0419319{col 38}{space 2} .0075558{col 49}{space 1}    5.55{col 58}{space 3}0.000{col 66}{space 4} .0271229{col 79}{space 3} .0567409
{txt}{space 19}2010  {c |}{col 26}{res}{space 2} .0737506{col 38}{space 2} .0086219{col 49}{space 1}    8.55{col 58}{space 3}0.000{col 66}{space 4}  .056852{col 79}{space 3} .0906492
{txt}{space 19}2011  {c |}{col 26}{res}{space 2} .0490705{col 38}{space 2} .0074176{col 49}{space 1}    6.62{col 58}{space 3}0.000{col 66}{space 4} .0345322{col 79}{space 3} .0636088
{txt}{space 19}2012  {c |}{col 26}{res}{space 2} .0349238{col 38}{space 2} .0069515{col 49}{space 1}    5.02{col 58}{space 3}0.000{col 66}{space 4} .0212991{col 79}{space 3} .0485484
{txt}{space 19}2013  {c |}{col 26}{res}{space 2} .0341745{col 38}{space 2} .0072837{col 49}{space 1}    4.69{col 58}{space 3}0.000{col 66}{space 4} .0198988{col 79}{space 3} .0484503
{txt}{space 19}2014  {c |}{col 26}{res}{space 2} .0380387{col 38}{space 2} .0079218{col 49}{space 1}    4.80{col 58}{space 3}0.000{col 66}{space 4} .0225123{col 79}{space 3} .0535651
{txt}{space 19}2015  {c |}{col 26}{res}{space 2} .0366123{col 38}{space 2} .0085002{col 49}{space 1}    4.31{col 58}{space 3}0.000{col 66}{space 4} .0199522{col 79}{space 3} .0532724
{txt}{space 19}2016  {c |}{col 26}{res}{space 2} .0423647{col 38}{space 2} .0081031{col 49}{space 1}    5.23{col 58}{space 3}0.000{col 66}{space 4}  .026483{col 79}{space 3} .0582465
{txt}{space 19}2017  {c |}{col 26}{res}{space 2} .0578853{col 38}{space 2} .0085723{col 49}{space 1}    6.75{col 58}{space 3}0.000{col 66}{space 4} .0410839{col 79}{space 3} .0746867
{txt}{space 19}2018  {c |}{col 26}{res}{space 2} .0586752{col 38}{space 2} .0091626{col 49}{space 1}    6.40{col 58}{space 3}0.000{col 66}{space 4} .0407169{col 79}{space 3} .0766336
{txt}{space 19}2019  {c |}{col 26}{res}{space 2} .0535486{col 38}{space 2} .0096389{col 49}{space 1}    5.56{col 58}{space 3}0.000{col 66}{space 4} .0346567{col 79}{space 3} .0724406
{txt}{space 19}2020  {c |}{col 26}{res}{space 2} .0769582{col 38}{space 2} .0144482{col 49}{space 1}    5.33{col 58}{space 3}0.000{col 66}{space 4} .0486403{col 79}{space 3} .1052762
{txt}{space 19}2021  {c |}{col 26}{res}{space 2} .2004118{col 38}{space 2} .0179746{col 49}{space 1}   11.15{col 58}{space 3}0.000{col 66}{space 4} .1651822{col 79}{space 3} .2356414
{txt}{space 19}2022  {c |}{col 26}{res}{space 2} .1625722{col 38}{space 2} .0180855{col 49}{space 1}    8.99{col 58}{space 3}0.000{col 66}{space 4} .1271254{col 79}{space 3} .1980191
{txt}{space 24} {c |}
adoptcohort_2002_itadopt {c |}{col 26}{res}{space 2} .1394173{col 38}{space 2}  .027179{col 49}{space 1}    5.13{col 58}{space 3}0.000{col 66}{space 4} .0861475{col 79}{space 3} .1926871
{txt}adoptcohort_2004_itadopt {c |}{col 26}{res}{space 2} .0256913{col 38}{space 2} .0200433{col 49}{space 1}    1.28{col 58}{space 3}0.200{col 66}{space 4}-.0135929{col 79}{space 3} .0649755
{txt}adoptcohort_2006_itadopt {c |}{col 26}{res}{space 2} .0083929{col 38}{space 2} .0121683{col 49}{space 1}    0.69{col 58}{space 3}0.490{col 66}{space 4}-.0154566{col 79}{space 3} .0322424
{txt}adoptcohort_2007_itadopt {c |}{col 26}{res}{space 2} .0203892{col 38}{space 2} .0128123{col 49}{space 1}    1.59{col 58}{space 3}0.112{col 66}{space 4}-.0047225{col 79}{space 3} .0455009
{txt}adoptcohort_2009_itadopt {c |}{col 26}{res}{space 2} .1134231{col 38}{space 2} .0114793{col 49}{space 1}    9.88{col 58}{space 3}0.000{col 66}{space 4} .0909241{col 79}{space 3} .1359222
{txt}adoptcohort_2010_itadopt {c |}{col 26}{res}{space 2} .0699391{col 38}{space 2} .0141734{col 49}{space 1}    4.93{col 58}{space 3}0.000{col 66}{space 4} .0421597{col 79}{space 3} .0977185
{txt}adoptcohort_2013_itadopt {c |}{col 26}{res}{space 2} .0426115{col 38}{space 2} .0098388{col 49}{space 1}    4.33{col 58}{space 3}0.000{col 66}{space 4} .0233279{col 79}{space 3} .0618951
{txt}adoptcohort_2014_itadopt {c |}{col 26}{res}{space 2}-.1071608{col 38}{space 2} .0273206{col 49}{space 1}   -3.92{col 58}{space 3}0.000{col 66}{space 4}-.1607082{col 79}{space 3}-.0536134
{txt}adoptcohort_2015_itadopt {c |}{col 26}{res}{space 2} -.025136{col 38}{space 2} .0127367{col 49}{space 1}   -1.97{col 58}{space 3}0.048{col 66}{space 4}-.0500994{col 79}{space 3}-.0001726
{txt}adoptcohort_2016_itadopt {c |}{col 26}{res}{space 2}-.0837526{col 38}{space 2} .0102604{col 49}{space 1}   -8.16{col 58}{space 3}0.000{col 66}{space 4}-.1038626{col 79}{space 3}-.0636427
{txt}adoptcohort_2017_itadopt {c |}{col 26}{res}{space 2}-.0048427{col 38}{space 2} .0133811{col 49}{space 1}   -0.36{col 58}{space 3}0.717{col 66}{space 4}-.0310691{col 79}{space 3} .0213837
{txt}adoptcohort_2018_itadopt {c |}{col 26}{res}{space 2}-.0580166{col 38}{space 2} .0137605{col 49}{space 1}   -4.22{col 58}{space 3}0.000{col 66}{space 4}-.0849866{col 79}{space 3}-.0310465
{txt}adoptcohort_2020_itadopt {c |}{col 26}{res}{space 2} .0645143{col 38}{space 2} .0235569{col 49}{space 1}    2.74{col 58}{space 3}0.006{col 66}{space 4} .0183436{col 79}{space 3} .1106849
{txt}adoptcohort_2021_itadopt {c |}{col 26}{res}{space 2}-.0537963{col 38}{space 2} .0326817{col 49}{space 1}   -1.65{col 58}{space 3}0.100{col 66}{space 4}-.1178513{col 79}{space 3} .0102586
{txt}{hline 25}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. 
. 
. ** COMPUTE PSEUDO R^2 [SSE / (SSE + SSR) = EXPLAINED/PREDICTED SUM OF SQUARES / (EXPLAINED/PREDICTED SUM OF SQUARES + RESIDUAL SUM OF SQUARES)] = SSE / SST
. 
. predict predsy_m1b if e(sample)
{txt}(statistic {bf:mean} assumed; mean function)
{res}{txt}
{com}. predict residsy_m1b if e(sample), residuals
{res}{txt}(5,551 missing values generated)

{com}. 
. gen sse_m1b = predsy_m1b * predsy_m1b if e(sample)
{txt}(5,551 missing values generated)

{com}. gen ssr_m1b = residsy_m1b * residsy_m1b if e(sample)
{txt}(5,551 missing values generated)

{com}. 
. egen sum_sse_m1b = total(sse_m1b) if e(sample)
{txt}(5,551 missing values generated)

{com}. egen sum_ssr_m1b = total(ssr_m1b) if e(sample)
{txt}(5,551 missing values generated)

{com}. 
. gen r2_m1b = sum_ssr_m1b/(sum_sse_m1b + sum_ssr_m1b)
{txt}(5,551 missing values generated)

{com}. 
. sum r2_m1b

{txt}    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 6}r2_m1b {c |}{res}      7,000    .2271317           0   .2271317   .2271317
{txt}
{com}. 
. *
. *
. *
. *
. *
. 
. * [MODEL B1: TOTAL PROGRAM ERROR RATE] FIGURE B1A:  UNCONDITIONAL ADAPTATION EFFECTS -- E(Y) [WITH RESPECT TO MONTHS SINCE ADOPTION (t + k) : 0 1 6 12.....60] 
. 
. margins, at(itmod_monthcount=(0 1 6 12 18 24 30 36 42 48 54 60))
{res}
{txt}{col 1}Predictive margins{col 58}{lalign 13:Number of obs}{col 71} = {res}{ralign 5:7,000}
{txt}{col 1}Model VCE: {res:Bootstrap}

{txt}{p2colset 1 13 13 2}{...}
{p2col:Expression:}{res:Mean function, predict()}{p_end}
{p2colreset}{...}
{lalign 8:1._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:0}}
{lalign 8:2._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:1}}
{lalign 8:3._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:6}}
{lalign 8:4._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:12}}
{lalign 8:5._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:18}}
{lalign 8:6._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:24}}
{lalign 8:7._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:30}}
{lalign 8:8._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:36}}
{lalign 8:9._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:42}}
{lalign 8:10._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:48}}
{lalign 8:11._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:54}}
{lalign 8:12._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:60}}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}     Margin{col 26}   std. err.{col 38}      z{col 46}   P>|z|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}_at {c |}
{space 10}1  {c |}{col 14}{res}{space 2} .1917917{col 26}{space 2} .0035005{col 37}{space 1}   54.79{col 46}{space 3}0.000{col 54}{space 4} .1849308{col 67}{space 3} .1986526
{txt}{space 10}2  {c |}{col 14}{res}{space 2} .1908536{col 26}{space 2} .0032121{col 37}{space 1}   59.42{col 46}{space 3}0.000{col 54}{space 4} .1845579{col 67}{space 3} .1971492
{txt}{space 10}3  {c |}{col 14}{res}{space 2} .1863083{col 26}{space 2} .0021182{col 37}{space 1}   87.96{col 46}{space 3}0.000{col 54}{space 4} .1821567{col 67}{space 3} .1904598
{txt}{space 10}4  {c |}{col 14}{res}{space 2} .1811568{col 26}{space 2} .0019995{col 37}{space 1}   90.60{col 46}{space 3}0.000{col 54}{space 4} .1772379{col 67}{space 3} .1850758
{txt}{space 10}5  {c |}{col 14}{res}{space 2} .1763098{col 26}{space 2}  .002846{col 37}{space 1}   61.95{col 46}{space 3}0.000{col 54}{space 4} .1707318{col 67}{space 3} .1818878
{txt}{space 10}6  {c |}{col 14}{res}{space 2} .1717395{col 26}{space 2} .0038401{col 37}{space 1}   44.72{col 46}{space 3}0.000{col 54}{space 4} .1642131{col 67}{space 3}  .179266
{txt}{space 10}7  {c |}{col 14}{res}{space 2} .1674184{col 26}{space 2} .0047371{col 37}{space 1}   35.34{col 46}{space 3}0.000{col 54}{space 4} .1581338{col 67}{space 3}  .176703
{txt}{space 10}8  {c |}{col 14}{res}{space 2} .1633188{col 26}{space 2} .0054903{col 37}{space 1}   29.75{col 46}{space 3}0.000{col 54}{space 4}  .152558{col 67}{space 3} .1740796
{txt}{space 10}9  {c |}{col 14}{res}{space 2} .1594131{col 26}{space 2} .0060985{col 37}{space 1}   26.14{col 46}{space 3}0.000{col 54}{space 4} .1474602{col 67}{space 3}  .171366
{txt}{space 9}10  {c |}{col 14}{res}{space 2} .1556736{col 26}{space 2} .0065747{col 37}{space 1}   23.68{col 46}{space 3}0.000{col 54}{space 4} .1427875{col 67}{space 3} .1685597
{txt}{space 9}11  {c |}{col 14}{res}{space 2} .1520728{col 26}{space 2} .0069368{col 37}{space 1}   21.92{col 46}{space 3}0.000{col 54}{space 4}  .138477{col 67}{space 3} .1656686
{txt}{space 9}12  {c |}{col 14}{res}{space 2}  .148583{col 26}{space 2} .0072053{col 37}{space 1}   20.62{col 46}{space 3}0.000{col 54}{space 4} .1344609{col 67}{space 3} .1627051
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. marginsplot, recast(connected) ciopt(color(%40)) recastci(rarea) ///
> legend(on order(1 "Unconditional Adaptation") pos(6) ring(2) cols(2) size(9pt))  ///
> title(" {c -(}bf:FIGURE B1A{c )-}""{c -(}bf:Unconditional Adaptation Effect{c )-}" "{c -(}bf:(Total Program Error Rate [MODEL B1]){c )-}", size(10pt) linegap(0.7) margin(t+1 b+2 r-6)) ///
> xtitle("Months since IT Reform", size(10pt) margin(t+2 b+2)) ///
> ytitle("Total Program Error Rate", size(10pt) margin(r+2)) ///
> xlabel(0 "0" 1 "1" 6 "6" 12 "12" 18 "18" 24 "24" 30 "30" 36 "36" 42 "42" 48 "48" 54 "54" 60 "60", labsize(9pt) ) ///
> ylabel(, labsize(9pt) format(%9.2f) angle(0)) xsize(6)
{res}
{text}{p 0 0 2}Variables that uniquely identify margins: {bf:itmod_monthcount}{p_end}
{res}{txt}
{com}. *
. *
. graph save "Graph" "C:\Users\gk57526\Dropbox\Administration of UI Programs in the American States (Ji-Hyeun Hong)\Performance Management Project (Paper #3)\EMPIRICS\GRAPHICS\marginsplot.Model B1.FIGURE B1A.04-10-2025.gph", replace
{txt}{p 0 4 2}
(file {bf}
C:\Users\gk57526\Dropbox\Administration of UI Programs in the American States (Ji-Hyeun Hong)\Performance Management Project (Paper #3)\EMPIRICS\GRAPHICS\marginsplot.Model B1.FIGURE B1A.04-10-2025.gph{rm}
not found)
{p_end}
{res}{txt}file {bf:C:\Users\gk57526\Dropbox\Administration of UI Programs in the American States (Ji-Hyeun Hong)\Performance Management Project (Paper #3)\EMPIRICS\GRAPHICS\marginsplot.Model B1.FIGURE B1A.04-10-2025.gph} saved

{com}. 
. *
. *
. *
. 
. * [MODEL B1: TOTAL PROGRAM ERROR RATE] FIGURE B1B: MARGINAL DIFFERENTIAL EFFECT BETWEEN HIGH TASK COMPLEXITY (tot_interstate_cat==2) & LOW COMPLEXITY (tot_interstate_cat==0) VALUES [WITH RESPECT TO MONTHS SINCE ADOPTION (t + k) : 0 1 6 12.....60]: ***
. 
. margins r.tot_interstate_cat if tot_interstate_cat==0|tot_interstate_cat==2, at(itmod_monthcount=(0 1 6 12 18 24 30 36 42 48 54 60))
{res}
{txt}{col 1}Contrasts of predictive margins{col 58}{lalign 13:Number of obs}{col 71} = {res}{ralign 5:3,575}
{txt}{col 1}Model VCE: {res:Bootstrap}

{txt}{p2colset 1 13 13 2}{...}
{p2col:Expression:}{res:Mean function, predict()}{p_end}
{p2colreset}{...}
{lalign 8:1._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:0}}
{lalign 8:2._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:1}}
{lalign 8:3._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:6}}
{lalign 8:4._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:12}}
{lalign 8:5._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:18}}
{lalign 8:6._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:24}}
{lalign 8:7._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:30}}
{lalign 8:8._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:36}}
{lalign 8:9._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:42}}
{lalign 8:10._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:48}}
{lalign 8:11._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:54}}
{lalign 8:12._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:60}}
{res}
{col 1}{text}{hline 23}{c TT}{hline 11}{hline 12}{hline 11}
{col 24}{text}{c |}         df{col 36}        chi2{col 48}     P>chi2
{res}{col 1}{text}{hline 23}{c +}{hline 11}{hline 12}{hline 11}
tot_interstate_cat@_at {c |}
{space 10}(2 vs 0)  1  {res}{col 24}{text}{c |}{result}{space 2}        1{col 36}{space 3}     0.14{col 48}{space 2}   0.7049
{txt}{space 10}(2 vs 0)  2  {res}{col 24}{text}{c |}{result}{space 2}        1{col 36}{space 3}     0.27{col 48}{space 2}   0.6037
{txt}{space 10}(2 vs 0)  3  {res}{col 24}{text}{c |}{result}{space 2}        1{col 36}{space 3}     1.23{col 48}{space 2}   0.2664
{txt}{space 10}(2 vs 0)  4  {res}{col 24}{text}{c |}{result}{space 2}        1{col 36}{space 3}     1.95{col 48}{space 2}   0.1623
{txt}{space 10}(2 vs 0)  5  {res}{col 24}{text}{c |}{result}{space 2}        1{col 36}{space 3}     1.84{col 48}{space 2}   0.1755
{txt}{space 10}(2 vs 0)  6  {res}{col 24}{text}{c |}{result}{space 2}        1{col 36}{space 3}     1.38{col 48}{space 2}   0.2409
{txt}{space 10}(2 vs 0)  7  {res}{col 24}{text}{c |}{result}{space 2}        1{col 36}{space 3}     0.85{col 48}{space 2}   0.3562
{txt}{space 10}(2 vs 0)  8  {res}{col 24}{text}{c |}{result}{space 2}        1{col 36}{space 3}     0.39{col 48}{space 2}   0.5337
{txt}{space 10}(2 vs 0)  9  {res}{col 24}{text}{c |}{result}{space 2}        1{col 36}{space 3}     0.07{col 48}{space 2}   0.7844
{txt}{space 10}(2 vs 0) 10  {res}{col 24}{text}{c |}{result}{space 2}        1{col 36}{space 3}     0.02{col 48}{space 2}   0.9006
{txt}{space 10}(2 vs 0) 11  {res}{col 24}{text}{c |}{result}{space 2}        1{col 36}{space 3}     0.33{col 48}{space 2}   0.5678
{txt}{space 10}(2 vs 0) 12  {res}{col 24}{text}{c |}{result}{space 2}        1{col 36}{space 3}     1.12{col 48}{space 2}   0.2905
{col 1}{text}                Joint {col 24}{c |}{result}  (not testable)
{col 1}{text}{hline 23}{c BT}{hline 11}{hline 12}{hline 11}
{res}
{txt}{hline 23}{c TT}{hline 11}{hline 11}{hline 14}{hline 12}
{col 24}{c |}{col 36} Delta-method
{col 24}{c |}   Contrast{col 36}   std. err.{col 48}     [95% con{col 61}f. interval]
{hline 23}{c +}{hline 11}{hline 11}{hline 14}{hline 12}
tot_interstate_cat@_at {c |}
{space 10}(2 vs 0)  1  {c |}{col 24}{res}{space 2} .0021977{col 36}{space 2} .0058033{col 47}{space 5}-.0091766{col 61}{space 3} .0135721
{txt}{space 10}(2 vs 0)  2  {c |}{col 24}{res}{space 2} .0029202{col 36}{space 2} .0056261{col 47}{space 5}-.0081067{col 61}{space 3} .0139471
{txt}{space 10}(2 vs 0)  3  {c |}{col 24}{res}{space 2} .0059147{col 36}{space 2} .0053224{col 47}{space 5} -.004517{col 61}{space 3} .0163463
{txt}{space 10}(2 vs 0)  4  {c |}{col 24}{res}{space 2} .0082185{col 36}{space 2} .0058815{col 47}{space 5} -.003309{col 61}{space 3}  .019746
{txt}{space 10}(2 vs 0)  5  {c |}{col 24}{res}{space 2} .0092241{col 36}{space 2}  .006809{col 47}{space 5}-.0041212{col 61}{space 3} .0225694
{txt}{space 10}(2 vs 0)  6  {c |}{col 24}{res}{space 2} .0090464{col 36}{space 2} .0077135{col 47}{space 5}-.0060718{col 61}{space 3} .0241646
{txt}{space 10}(2 vs 0)  7  {c |}{col 24}{res}{space 2} .0078003{col 36}{space 2} .0084536{col 47}{space 5}-.0087684{col 61}{space 3}  .024369
{txt}{space 10}(2 vs 0)  8  {c |}{col 24}{res}{space 2} .0056007{col 36}{space 2} .0089992{col 47}{space 5}-.0120374{col 61}{space 3} .0232388
{txt}{space 10}(2 vs 0)  9  {c |}{col 24}{res}{space 2} .0025625{col 36}{space 2}  .009368{col 47}{space 5}-.0157984{col 61}{space 3} .0209234
{txt}{space 10}(2 vs 0) 10  {c |}{col 24}{res}{space 2}-.0011994{col 36}{space 2} .0096004{col 47}{space 5}-.0200158{col 61}{space 3}  .017617
{txt}{space 10}(2 vs 0) 11  {c |}{col 24}{res}{space 2}-.0055701{col 36}{space 2}  .009749{col 47}{space 5}-.0246778{col 61}{space 3} .0135376
{txt}{space 10}(2 vs 0) 12  {c |}{col 24}{res}{space 2}-.0104348{col 36}{space 2} .0098723{col 47}{space 5}-.0297841{col 61}{space 3} .0089146
{txt}{hline 23}{c BT}{hline 11}{hline 11}{hline 14}{hline 12}
{res}{txt}
{com}. 
. marginsplot, recast(connected) ciopt(color(%40)) recastci(rarea) /// 
> yline(0, lcolor(%40gs) lpattern(shortdash)) ///
> legend(on order(1 "High Task Complexity - Low Task Complexity") pos(6) ring(2) cols(2) size(10pt))  ///
> title(" {c -(}bf:FIGURE B1B{c )-}""{c -(}bf:Conditional Adaptation Maiginal Effect By Task Complexity{c )-}" "{c -(}bf:(Interstate Claims: Total Program Error Rate [MODEL B1]){c )-}", size(10pt) linegap(0.7) margin(t+1 b+1 r-6)) ///
> xtitle("Months since IT Reform", size(10pt) margin(t+2 b+2)) ///
> ytitle("Total Program Error Rate", size(10pt) margin(r+2)) ///
> xlabel(0 "0" 1 "1" 6 "6" 12 "12" 18 "18" 24 "24" 30 "30" 36 "36" 42 "42" 48 "48" 54 "54" 60 "60", labsize(9pt) ) ///
> ylabel(, labsize(9pt) format(%9.2f) angle(0)) xsize(6)
{res}
{text}{p 0 0 2}Variables that uniquely identify margins: {bf:itmod_monthcount}{p_end}
{p 0 4 2}
{txt}(note:  named style
% 40gs not found in class
color,  default attributes used)
{p_end}
{res}{txt}
{com}. *
. *
. *
. graph save "Graph" "C:\Users\gk57526\Dropbox\Administration of UI Programs in the American States (Ji-Hyeun Hong)\Performance Management Project (Paper #3)\EMPIRICS\GRAPHICS\marginsplot.Model B1.FIGURE B1B.04-10-2025.gph", replace
{txt}{p 0 4 2}
(file {bf}
C:\Users\gk57526\Dropbox\Administration of UI Programs in the American States (Ji-Hyeun Hong)\Performance Management Project (Paper #3)\EMPIRICS\GRAPHICS\marginsplot.Model B1.FIGURE B1B.04-10-2025.gph{rm}
not found)
{p_end}
{res}{txt}file {bf:C:\Users\gk57526\Dropbox\Administration of UI Programs in the American States (Ji-Hyeun Hong)\Performance Management Project (Paper #3)\EMPIRICS\GRAPHICS\marginsplot.Model B1.FIGURE B1B.04-10-2025.gph} saved

{com}. *
. *
. *
. 
. * [MODEL B1: TOTAL PROGRAM ERROR RATE] FIGURE B1C:  MARGINAL DIFFERENTIAL EFFECT BETWEEN HIGH TASK COMPLEXITY (tot_diffoccupseek_cat==2) & LOW COMPLEXITY (tot_extbenefits_cat==0) VALUES [WITH RESPECT TO MONTHS SINCE ADOPTION (t + k) : 0 1 6 12.....60]: ***
. 
. margins r.tot_diffoccupseek_cat if tot_diffoccupseek_cat==0|tot_diffoccupseek_cat==2, at(itmod_monthcount=(0 1 6 12 18 24 30 36 42 48 54 60))
{res}
{txt}{col 1}Contrasts of predictive margins{col 58}{lalign 13:Number of obs}{col 71} = {res}{ralign 5:3,501}
{txt}{col 1}Model VCE: {res:Bootstrap}

{txt}{p2colset 1 13 13 2}{...}
{p2col:Expression:}{res:Mean function, predict()}{p_end}
{p2colreset}{...}
{lalign 8:1._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:0}}
{lalign 8:2._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:1}}
{lalign 8:3._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:6}}
{lalign 8:4._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:12}}
{lalign 8:5._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:18}}
{lalign 8:6._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:24}}
{lalign 8:7._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:30}}
{lalign 8:8._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:36}}
{lalign 8:9._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:42}}
{lalign 8:10._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:48}}
{lalign 8:11._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:54}}
{lalign 8:12._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:60}}
{res}
{col 1}{text}{hline 26}{c TT}{hline 11}{hline 12}{hline 11}
{col 27}{text}{c |}         df{col 39}        chi2{col 51}     P>chi2
{res}{col 1}{text}{hline 26}{c +}{hline 11}{hline 12}{hline 11}
tot_diffoccupseek_cat@_at {c |}
{space 13}(2 vs 0)  1  {res}{col 27}{text}{c |}{result}{space 2}        1{col 39}{space 3}    42.48{col 51}{space 2}   0.0000
{txt}{space 13}(2 vs 0)  2  {res}{col 27}{text}{c |}{result}{space 2}        1{col 39}{space 3}    43.10{col 51}{space 2}   0.0000
{txt}{space 13}(2 vs 0)  3  {res}{col 27}{text}{c |}{result}{space 2}        1{col 39}{space 3}    33.56{col 51}{space 2}   0.0000
{txt}{space 13}(2 vs 0)  4  {res}{col 27}{text}{c |}{result}{space 2}        1{col 39}{space 3}    16.29{col 51}{space 2}   0.0001
{txt}{space 13}(2 vs 0)  5  {res}{col 27}{text}{c |}{result}{space 2}        1{col 39}{space 3}     7.38{col 51}{space 2}   0.0066
{txt}{space 13}(2 vs 0)  6  {res}{col 27}{text}{c |}{result}{space 2}        1{col 39}{space 3}     3.37{col 51}{space 2}   0.0665
{txt}{space 13}(2 vs 0)  7  {res}{col 27}{text}{c |}{result}{space 2}        1{col 39}{space 3}     1.45{col 51}{space 2}   0.2284
{txt}{space 13}(2 vs 0)  8  {res}{col 27}{text}{c |}{result}{space 2}        1{col 39}{space 3}     0.51{col 51}{space 2}   0.4769
{txt}{space 13}(2 vs 0)  9  {res}{col 27}{text}{c |}{result}{space 2}        1{col 39}{space 3}     0.09{col 51}{space 2}   0.7688
{txt}{space 13}(2 vs 0) 10  {res}{col 27}{text}{c |}{result}{space 2}        1{col 39}{space 3}     0.01{col 51}{space 2}   0.9355
{txt}{space 13}(2 vs 0) 11  {res}{col 27}{text}{c |}{result}{space 2}        1{col 39}{space 3}     0.19{col 51}{space 2}   0.6671
{txt}{space 13}(2 vs 0) 12  {res}{col 27}{text}{c |}{result}{space 2}        1{col 39}{space 3}     0.58{col 51}{space 2}   0.4480
{col 1}{text}                   Joint {col 27}{c |}{result}  (not testable)
{col 1}{text}{hline 26}{c BT}{hline 11}{hline 12}{hline 11}
{res}
{txt}{hline 26}{c TT}{hline 11}{hline 11}{hline 14}{hline 12}
{col 27}{c |}{col 39} Delta-method
{col 27}{c |}   Contrast{col 39}   std. err.{col 51}     [95% con{col 64}f. interval]
{hline 26}{c +}{hline 11}{hline 11}{hline 14}{hline 12}
tot_diffoccupseek_cat@_at {c |}
{space 13}(2 vs 0)  1  {c |}{col 27}{res}{space 2} .0316111{col 39}{space 2} .0048501{col 50}{space 5} .0221051{col 64}{space 3} .0411171
{txt}{space 13}(2 vs 0)  2  {c |}{col 27}{res}{space 2} .0308964{col 39}{space 2} .0047059{col 50}{space 5} .0216729{col 64}{space 3} .0401198
{txt}{space 13}(2 vs 0)  3  {c |}{col 27}{res}{space 2} .0273356{col 39}{space 2} .0047185{col 50}{space 5} .0180875{col 64}{space 3} .0365837
{txt}{space 13}(2 vs 0)  4  {c |}{col 27}{res}{space 2}  .023099{col 39}{space 2} .0057237{col 50}{space 5} .0118808{col 64}{space 3} .0343172
{txt}{space 13}(2 vs 0)  5  {c |}{col 27}{res}{space 2} .0189134{col 39}{space 2} .0069618{col 50}{space 5} .0052686{col 64}{space 3} .0325583
{txt}{space 13}(2 vs 0)  6  {c |}{col 27}{res}{space 2}  .014791{col 39}{space 2}   .00806{col 50}{space 5}-.0010063{col 64}{space 3} .0305883
{txt}{space 13}(2 vs 0)  7  {c |}{col 27}{res}{space 2} .0107438{col 39}{space 2} .0089201{col 50}{space 5}-.0067394{col 64}{space 3} .0282269
{txt}{space 13}(2 vs 0)  8  {c |}{col 27}{res}{space 2} .0067838{col 39}{space 2} .0095382{col 50}{space 5}-.0119107{col 64}{space 3} .0254784
{txt}{space 13}(2 vs 0)  9  {c |}{col 27}{res}{space 2} .0029233{col 39}{space 2} .0099468{col 50}{space 5}-.0165721{col 64}{space 3} .0224187
{txt}{space 13}(2 vs 0) 10  {c |}{col 27}{res}{space 2}-.0008258{col 39}{space 2} .0101963{col 50}{space 5}-.0208102{col 64}{space 3} .0191586
{txt}{space 13}(2 vs 0) 11  {c |}{col 27}{res}{space 2}-.0044512{col 39}{space 2} .0103473{col 50}{space 5}-.0247317{col 64}{space 3} .0158292
{txt}{space 13}(2 vs 0) 12  {c |}{col 27}{res}{space 2} -.007941{col 39}{space 2}  .010466{col 50}{space 5} -.028454{col 64}{space 3} .0125719
{txt}{hline 26}{c BT}{hline 11}{hline 11}{hline 14}{hline 12}
{res}{txt}
{com}. 
. marginsplot, recast(connected) ciopt(color(%40)) recastci(rarea) /// 
> yline(0, lcolor(%40gs) lpattern(shortdash)) ///
> legend(on order(1 "High Task Complexity - Low Task Complexity") pos(6) ring(2) cols(2) size(10pt))  ///
> title(" {c -(}bf:FIGURE B1C{c )-}""{c -(}bf:Conditional Adaptation Marginal Effect By Task Complexity{c )-}" "{c -(}bf:(Seeking Different Occupation: Total Program Error Rate [MODEL B1]){c )-}", size(10pt) linegap(0.7) margin(t+1 b+1 r-6)) ///
> xtitle("Months since IT Reform", size(10pt) margin(t+2 b+2)) ///
> ytitle("Total Program Error Rate", size(10pt) margin(r+2)) ///
> xlabel(0 "0" 1 "1" 6 "6" 12 "12" 18 "18" 24 "24" 30 "30" 36 "36" 42 "42" 48 "48" 54 "54" 60 "60", labsize(9pt) ) ///
> ylabel(, labsize(9pt) format(%9.2f) angle(0)) xsize(6)
{res}
{text}{p 0 0 2}Variables that uniquely identify margins: {bf:itmod_monthcount}{p_end}
{p 0 4 2}
{txt}(note:  named style
% 40gs not found in class
color,  default attributes used)
{p_end}
{res}{txt}
{com}. *
. *
. *
. graph save "Graph" "C:\Users\gk57526\Dropbox\Administration of UI Programs in the American States (Ji-Hyeun Hong)\Performance Management Project (Paper #3)\EMPIRICS\GRAPHICS\marginsplot.Model B1.FIGURE B1C.04-10-2025.gph", replace
{txt}{p 0 4 2}
(file {bf}
C:\Users\gk57526\Dropbox\Administration of UI Programs in the American States (Ji-Hyeun Hong)\Performance Management Project (Paper #3)\EMPIRICS\GRAPHICS\marginsplot.Model B1.FIGURE B1C.04-10-2025.gph{rm}
not found)
{p_end}
{res}{txt}file {bf:C:\Users\gk57526\Dropbox\Administration of UI Programs in the American States (Ji-Hyeun Hong)\Performance Management Project (Paper #3)\EMPIRICS\GRAPHICS\marginsplot.Model B1.FIGURE B1C.04-10-2025.gph} saved

{com}. 
. 
. 
. ******************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************
. 
. 
. 
. 
. *** TESTING H2 & H4: ABSOLUTE TYPE I ERROR RATE ORGANIZATIONAL ADAPTATION  ***
. 
. 
. 
. 
. *** ESTIMATE MODEL B2: ABSOLUTE TYPE I ERROR RATE [MODEL 2 omitting ADDITIONAL COVARIATES: PROPORTION OF TOTAL ERRORS VIA WEEKLY BAM SURVEY AGGREGATED TO MONTHLY OBSERVATIONS: [ONLY STATE, YEAR, AND YEAR-ADOPTION COHORT*TREATMENT UNIT EFFECTS] ***         (FIGURES B2A-B2C) 
. 
. 
. npregress series  t1error_rat  itmod_monthcount  i.t1_interstate_cat  i.t1_diffoccupseek_cat   if itmod_adopt_state==1, asis(i.stateid i.year  adoptcohort_2002_itadopt  adoptcohort_2004_itadopt  adoptcohort_2006_itadopt adoptcohort_2007_itadopt   adoptcohort_2009_itadopt  adoptcohort_2010_itadopt  adoptcohort_2013_itadopt  adoptcohort_2014_itadopt  adoptcohort_2015_itadopt  adoptcohort_2016_itadopt  adoptcohort_2017_itadopt  adoptcohort_2018_itadopt  adoptcohort_2020_itadopt  adoptcohort_2021_itadopt)  vce(bootstrap, seed(123) rep(1000))
{res}{txt}(running {bf:npregress} on estimation sample)
{res}
{text}Bootstrap replications ({result:1,000}){text}: 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{res}
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{txt}{space 24} {c |}
{space 20}year {c |}
{space 19}2003  {c |}{col 26}{res}{space 2} .0006851{col 38}{space 2} .0040113{col 49}{space 1}    0.17{col 58}{space 3}0.864{col 66}{space 4}-.0071769{col 79}{space 3} .0085472
{txt}{space 19}2004  {c |}{col 26}{res}{space 2}-.0032379{col 38}{space 2} .0038463{col 49}{space 1}   -0.84{col 58}{space 3}0.400{col 66}{space 4}-.0107765{col 79}{space 3} .0043006
{txt}{space 19}2005  {c |}{col 26}{res}{space 2}-.0035435{col 38}{space 2} .0040036{col 49}{space 1}   -0.89{col 58}{space 3}0.376{col 66}{space 4}-.0113905{col 79}{space 3} .0043035
{txt}{space 19}2006  {c |}{col 26}{res}{space 2}-.0009627{col 38}{space 2} .0040864{col 49}{space 1}   -0.24{col 58}{space 3}0.814{col 66}{space 4}-.0089719{col 79}{space 3} .0070465
{txt}{space 19}2007  {c |}{col 26}{res}{space 2} .0027955{col 38}{space 2} .0040139{col 49}{space 1}    0.70{col 58}{space 3}0.486{col 66}{space 4}-.0050717{col 79}{space 3} .0106627
{txt}{space 19}2008  {c |}{col 26}{res}{space 2} .0067174{col 38}{space 2} .0045132{col 49}{space 1}    1.49{col 58}{space 3}0.137{col 66}{space 4}-.0021283{col 79}{space 3} .0155632
{txt}{space 19}2009  {c |}{col 26}{res}{space 2} .0230316{col 38}{space 2} .0046944{col 49}{space 1}    4.91{col 58}{space 3}0.000{col 66}{space 4} .0138308{col 79}{space 3} .0322324
{txt}{space 19}2010  {c |}{col 26}{res}{space 2} .0374734{col 38}{space 2} .0055166{col 49}{space 1}    6.79{col 58}{space 3}0.000{col 66}{space 4}  .026661{col 79}{space 3} .0482859
{txt}{space 19}2011  {c |}{col 26}{res}{space 2} .0199212{col 38}{space 2} .0045843{col 49}{space 1}    4.35{col 58}{space 3}0.000{col 66}{space 4} .0109361{col 79}{space 3} .0289062
{txt}{space 19}2012  {c |}{col 26}{res}{space 2} .0218077{col 38}{space 2} .0043554{col 49}{space 1}    5.01{col 58}{space 3}0.000{col 66}{space 4} .0132712{col 79}{space 3} .0303442
{txt}{space 19}2013  {c |}{col 26}{res}{space 2}  .007511{col 38}{space 2} .0040735{col 49}{space 1}    1.84{col 58}{space 3}0.065{col 66}{space 4}-.0004729{col 79}{space 3} .0154948
{txt}{space 19}2014  {c |}{col 26}{res}{space 2} .0166166{col 38}{space 2} .0051065{col 49}{space 1}    3.25{col 58}{space 3}0.001{col 66}{space 4} .0066079{col 79}{space 3} .0266252
{txt}{space 19}2015  {c |}{col 26}{res}{space 2} .0250751{col 38}{space 2}  .005966{col 49}{space 1}    4.20{col 58}{space 3}0.000{col 66}{space 4} .0133819{col 79}{space 3} .0367683
{txt}{space 19}2016  {c |}{col 26}{res}{space 2} .0288362{col 38}{space 2} .0057599{col 49}{space 1}    5.01{col 58}{space 3}0.000{col 66}{space 4} .0175469{col 79}{space 3} .0401255
{txt}{space 19}2017  {c |}{col 26}{res}{space 2} .0382547{col 38}{space 2} .0063568{col 49}{space 1}    6.02{col 58}{space 3}0.000{col 66}{space 4} .0257955{col 79}{space 3} .0507139
{txt}{space 19}2018  {c |}{col 26}{res}{space 2} .0408191{col 38}{space 2}  .007337{col 49}{space 1}    5.56{col 58}{space 3}0.000{col 66}{space 4}  .026439{col 79}{space 3} .0551993
{txt}{space 19}2019  {c |}{col 26}{res}{space 2} .0399967{col 38}{space 2}  .007432{col 49}{space 1}    5.38{col 58}{space 3}0.000{col 66}{space 4} .0254302{col 79}{space 3} .0545632
{txt}{space 19}2020  {c |}{col 26}{res}{space 2}  .099977{col 38}{space 2}  .010545{col 49}{space 1}    9.48{col 58}{space 3}0.000{col 66}{space 4} .0793092{col 79}{space 3} .1206447
{txt}{space 19}2021  {c |}{col 26}{res}{space 2} .1533744{col 38}{space 2} .0114202{col 49}{space 1}   13.43{col 58}{space 3}0.000{col 66}{space 4} .1309911{col 79}{space 3} .1757576
{txt}{space 19}2022  {c |}{col 26}{res}{space 2} .1135379{col 38}{space 2} .0121939{col 49}{space 1}    9.31{col 58}{space 3}0.000{col 66}{space 4} .0896382{col 79}{space 3} .1374376
{txt}{space 24} {c |}
adoptcohort_2002_itadopt {c |}{col 26}{res}{space 2} .0793436{col 38}{space 2} .0106011{col 49}{space 1}    7.48{col 58}{space 3}0.000{col 66}{space 4} .0585658{col 79}{space 3} .1001214
{txt}adoptcohort_2004_itadopt {c |}{col 26}{res}{space 2} .0334899{col 38}{space 2} .0112734{col 49}{space 1}    2.97{col 58}{space 3}0.003{col 66}{space 4} .0113944{col 79}{space 3} .0555854
{txt}adoptcohort_2006_itadopt {c |}{col 26}{res}{space 2}  .025141{col 38}{space 2} .0073368{col 49}{space 1}    3.43{col 58}{space 3}0.001{col 66}{space 4} .0107611{col 79}{space 3} .0395209
{txt}adoptcohort_2007_itadopt {c |}{col 26}{res}{space 2} .0202385{col 38}{space 2} .0073503{col 49}{space 1}    2.75{col 58}{space 3}0.006{col 66}{space 4} .0058321{col 79}{space 3} .0346449
{txt}adoptcohort_2009_itadopt {c |}{col 26}{res}{space 2} .0048319{col 38}{space 2} .0055901{col 49}{space 1}    0.86{col 58}{space 3}0.387{col 66}{space 4}-.0061244{col 79}{space 3} .0157882
{txt}adoptcohort_2010_itadopt {c |}{col 26}{res}{space 2} -.000815{col 38}{space 2} .0069199{col 49}{space 1}   -0.12{col 58}{space 3}0.906{col 66}{space 4}-.0143777{col 79}{space 3} .0127477
{txt}adoptcohort_2013_itadopt {c |}{col 26}{res}{space 2} .0242705{col 38}{space 2} .0054743{col 49}{space 1}    4.43{col 58}{space 3}0.000{col 66}{space 4} .0135412{col 79}{space 3} .0349999
{txt}adoptcohort_2014_itadopt {c |}{col 26}{res}{space 2}-.0915083{col 38}{space 2} .0182806{col 49}{space 1}   -5.01{col 58}{space 3}0.000{col 66}{space 4}-.1273377{col 79}{space 3}-.0556789
{txt}adoptcohort_2015_itadopt {c |}{col 26}{res}{space 2}-.0316128{col 38}{space 2} .0077548{col 49}{space 1}   -4.08{col 58}{space 3}0.000{col 66}{space 4}-.0468119{col 79}{space 3}-.0164138
{txt}adoptcohort_2016_itadopt {c |}{col 26}{res}{space 2}-.0180126{col 38}{space 2} .0068262{col 49}{space 1}   -2.64{col 58}{space 3}0.008{col 66}{space 4}-.0313917{col 79}{space 3}-.0046335
{txt}adoptcohort_2017_itadopt {c |}{col 26}{res}{space 2}-.0436631{col 38}{space 2} .0069079{col 49}{space 1}   -6.32{col 58}{space 3}0.000{col 66}{space 4}-.0572024{col 79}{space 3}-.0301238
{txt}adoptcohort_2018_itadopt {c |}{col 26}{res}{space 2}-.0504518{col 38}{space 2} .0083846{col 49}{space 1}   -6.02{col 58}{space 3}0.000{col 66}{space 4}-.0668854{col 79}{space 3}-.0340182
{txt}adoptcohort_2020_itadopt {c |}{col 26}{res}{space 2} .0425998{col 38}{space 2} .0187996{col 49}{space 1}    2.27{col 58}{space 3}0.023{col 66}{space 4} .0057533{col 79}{space 3} .0794463
{txt}adoptcohort_2021_itadopt {c |}{col 26}{res}{space 2}-.0865745{col 38}{space 2} .0273029{col 49}{space 1}   -3.17{col 58}{space 3}0.002{col 66}{space 4}-.1400873{col 79}{space 3}-.0330617
{txt}{hline 25}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. 
. 
. ** COMPUTE PSEUDO R^2 [SSE / (SSE + SSR) = EXPLAINED/PREDICTED SUM OF SQUARES / (EXPLAINED/PREDICTED SUM OF SQUARES + RESIDUAL SUM OF SQUARES)] = SSE / SST
. 
. predict predsy_m2b if e(sample)
{txt}(statistic {bf:mean} assumed; mean function)
{res}{txt}
{com}. predict residsy_m2b if e(sample), residuals
{res}{txt}(5,289 missing values generated)

{com}. 
. gen sse_m2b = predsy_m2b * predsy_m2b if e(sample)
{txt}(5,289 missing values generated)

{com}. gen ssr_m2b = residsy_m2b * residsy_m2b if e(sample)
{txt}(5,289 missing values generated)

{com}. 
. egen sum_sse_m2b = total(sse_m2b) if e(sample)
{txt}(5,289 missing values generated)

{com}. egen sum_ssr_m2b = total(ssr_m2b) if e(sample)
{txt}(5,289 missing values generated)

{com}. 
. gen r2_m2b = sum_ssr_m2b/(sum_sse_m2b + sum_ssr_m2b)
{txt}(5,289 missing values generated)

{com}. 
. sum r2_m2b

{txt}    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 6}r2_m2b {c |}{res}      7,262    .4623641           0   .4623641   .4623641
{txt}
{com}. 
. *
. *
. *
. * [MODEL B2: ABSOLUTE TYPE I ERROR RATE] FIGURE B2A:  UNCONDITIONAL ADAPTATION EFFECTS -- E(Y) [WITH RESPECT TO MONTHS SINCE ADOPTION (t + k) : 0 1 6 12.....60]
. 
. margins, at(itmod_monthcount=(0 1 6 12 18 24 30 36 42 48 54 60))
{res}
{txt}{col 1}Predictive margins{col 58}{lalign 13:Number of obs}{col 71} = {res}{ralign 5:7,262}
{txt}{col 1}Model VCE: {res:Bootstrap}

{txt}{p2colset 1 13 13 2}{...}
{p2col:Expression:}{res:Mean function, predict()}{p_end}
{p2colreset}{...}
{lalign 8:1._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:0}}
{lalign 8:2._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:1}}
{lalign 8:3._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:6}}
{lalign 8:4._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:12}}
{lalign 8:5._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:18}}
{lalign 8:6._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:24}}
{lalign 8:7._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:30}}
{lalign 8:8._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:36}}
{lalign 8:9._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:42}}
{lalign 8:10._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:48}}
{lalign 8:11._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:54}}
{lalign 8:12._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:60}}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}     Margin{col 26}   std. err.{col 38}      z{col 46}   P>|z|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}_at {c |}
{space 10}1  {c |}{col 14}{res}{space 2} .0734957{col 26}{space 2} .0024286{col 37}{space 1}   30.26{col 46}{space 3}0.000{col 54}{space 4} .0687356{col 67}{space 3} .0782557
{txt}{space 10}2  {c |}{col 14}{res}{space 2} .0723134{col 26}{space 2} .0022857{col 37}{space 1}   31.64{col 46}{space 3}0.000{col 54}{space 4} .0678335{col 67}{space 3} .0767933
{txt}{space 10}3  {c |}{col 14}{res}{space 2} .0667399{col 26}{space 2} .0017093{col 37}{space 1}   39.05{col 46}{space 3}0.000{col 54}{space 4} .0633898{col 67}{space 3}   .07009
{txt}{space 10}4  {c |}{col 14}{res}{space 2} .0607596{col 26}{space 2} .0014213{col 37}{space 1}   42.75{col 46}{space 3}0.000{col 54}{space 4} .0579739{col 67}{space 3} .0635454
{txt}{space 10}5  {c |}{col 14}{res}{space 2} .0554961{col 26}{space 2} .0015881{col 37}{space 1}   34.94{col 46}{space 3}0.000{col 54}{space 4} .0523834{col 67}{space 3} .0586088
{txt}{space 10}6  {c |}{col 14}{res}{space 2} .0508906{col 26}{space 2} .0019769{col 37}{space 1}   25.74{col 46}{space 3}0.000{col 54}{space 4}  .047016{col 67}{space 3} .0547651
{txt}{space 10}7  {c |}{col 14}{res}{space 2} .0468843{col 26}{space 2} .0024061{col 37}{space 1}   19.49{col 46}{space 3}0.000{col 54}{space 4} .0421684{col 67}{space 3} .0516001
{txt}{space 10}8  {c |}{col 14}{res}{space 2} .0434185{col 26}{space 2} .0028087{col 37}{space 1}   15.46{col 46}{space 3}0.000{col 54}{space 4} .0379135{col 67}{space 3} .0489234
{txt}{space 10}9  {c |}{col 14}{res}{space 2} .0404344{col 26}{space 2} .0031656{col 37}{space 1}   12.77{col 46}{space 3}0.000{col 54}{space 4} .0342299{col 67}{space 3} .0466389
{txt}{space 9}10  {c |}{col 14}{res}{space 2} .0378733{col 26}{space 2} .0034751{col 37}{space 1}   10.90{col 46}{space 3}0.000{col 54}{space 4} .0310623{col 67}{space 3} .0446844
{txt}{space 9}11  {c |}{col 14}{res}{space 2} .0356765{col 26}{space 2} .0037421{col 37}{space 1}    9.53{col 46}{space 3}0.000{col 54}{space 4} .0283421{col 67}{space 3}  .043011
{txt}{space 9}12  {c |}{col 14}{res}{space 2} .0337852{col 26}{space 2} .0039747{col 37}{space 1}    8.50{col 46}{space 3}0.000{col 54}{space 4}  .025995{col 67}{space 3} .0415755
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. marginsplot, recast(connected) ciopt(color(%40)) recastci(rarea) ///
> legend(on order(1 "Unconditional Adaptation") pos(6) ring(2) cols(2) size(9pt))  ///
> title(" {c -(}bf:FIGURE B2A{c )-}""{c -(}bf:Unconditional Adaptation Effect{c )-}" "{c -(}bf:(Absolute Type I Program Error Rate [MODEL B2]){c )-}", size(10pt) linegap(0.7) margin(t+1 b+2 r-6)) ///
> xtitle("Months since IT Reform", size(10pt) margin(t+2 b+2)) ///
> ytitle("Absolute Type I Program Error Rate", size(10pt) margin(r+2)) ///
> xlabel(0 "0" 1 "1" 6 "6" 12 "12" 18 "18" 24 "24" 30 "30" 36 "36" 42 "42" 48 "48" 54 "54" 60 "60", labsize(9pt) ) ///
> ylabel(, labsize(9pt) format(%9.2f) angle(0)) xsize(6)
{res}
{text}{p 0 0 2}Variables that uniquely identify margins: {bf:itmod_monthcount}{p_end}
{res}{txt}
{com}. *
. *
. graph save "Graph" "C:\Users\gk57526\Dropbox\Administration of UI Programs in the American States (Ji-Hyeun Hong)\Performance Management Project (Paper #3)\EMPIRICS\GRAPHICS\marginsplot.Model B2.FIGURE B2A.04-10-2025.gph", replace
{txt}{p 0 4 2}
(file {bf}
C:\Users\gk57526\Dropbox\Administration of UI Programs in the American States (Ji-Hyeun Hong)\Performance Management Project (Paper #3)\EMPIRICS\GRAPHICS\marginsplot.Model B2.FIGURE B2A.04-10-2025.gph{rm}
not found)
{p_end}
{res}{txt}file {bf:C:\Users\gk57526\Dropbox\Administration of UI Programs in the American States (Ji-Hyeun Hong)\Performance Management Project (Paper #3)\EMPIRICS\GRAPHICS\marginsplot.Model B2.FIGURE B2A.04-10-2025.gph} saved

{com}. 
. *
. *
. *
. * [MODEL B2: ABSOLUTE TYPE I ERROR RATE] FIGURE B2B:  MARGINAL DIFFERENTIAL EFFECT BETWEEN HIGH TASK COMPLEXITY (t1_interstate_cat==2) & LOW COMPLEXITY (t1_interstate_cat==0) VALUES [WITH RESPECT TO MONTHS SINCE ADOPTION (t + k) : 0 1 6 12.....60]: ***
. 
. margins r.t1_interstate_cat if t1_interstate_cat==0|t1_interstate_cat==2, at(itmod_monthcount=(0 1 6 12 18 24 30 36 42 48 54 60))
{res}
{txt}{col 1}Contrasts of predictive margins{col 58}{lalign 13:Number of obs}{col 71} = {res}{ralign 5:3,767}
{txt}{col 1}Model VCE: {res:Bootstrap}

{txt}{p2colset 1 13 13 2}{...}
{p2col:Expression:}{res:Mean function, predict()}{p_end}
{p2colreset}{...}
{lalign 8:1._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:0}}
{lalign 8:2._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:1}}
{lalign 8:3._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:6}}
{lalign 8:4._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:12}}
{lalign 8:5._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:18}}
{lalign 8:6._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:24}}
{lalign 8:7._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:30}}
{lalign 8:8._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:36}}
{lalign 8:9._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:42}}
{lalign 8:10._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:48}}
{lalign 8:11._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:54}}
{lalign 8:12._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:60}}
{res}
{col 1}{text}{hline 22}{c TT}{hline 11}{hline 12}{hline 11}
{col 23}{text}{c |}         df{col 35}        chi2{col 47}     P>chi2
{res}{col 1}{text}{hline 22}{c +}{hline 11}{hline 12}{hline 11}
t1_interstate_cat@_at {c |}
{space 9}(2 vs 0)  1  {res}{col 23}{text}{c |}{result}{space 2}        1{col 35}{space 3}     0.45{col 47}{space 2}   0.5016
{txt}{space 9}(2 vs 0)  2  {res}{col 23}{text}{c |}{result}{space 2}        1{col 35}{space 3}     0.49{col 47}{space 2}   0.4822
{txt}{space 9}(2 vs 0)  3  {res}{col 23}{text}{c |}{result}{space 2}        1{col 35}{space 3}     0.62{col 47}{space 2}   0.4323
{txt}{space 9}(2 vs 0)  4  {res}{col 23}{text}{c |}{result}{space 2}        1{col 35}{space 3}     0.48{col 47}{space 2}   0.4878
{txt}{space 9}(2 vs 0)  5  {res}{col 23}{text}{c |}{result}{space 2}        1{col 35}{space 3}     0.23{col 47}{space 2}   0.6299
{txt}{space 9}(2 vs 0)  6  {res}{col 23}{text}{c |}{result}{space 2}        1{col 35}{space 3}     0.06{col 47}{space 2}   0.8063
{txt}{space 9}(2 vs 0)  7  {res}{col 23}{text}{c |}{result}{space 2}        1{col 35}{space 3}     0.00{col 47}{space 2}   0.9917
{txt}{space 9}(2 vs 0)  8  {res}{col 23}{text}{c |}{result}{space 2}        1{col 35}{space 3}     0.05{col 47}{space 2}   0.8232
{txt}{space 9}(2 vs 0)  9  {res}{col 23}{text}{c |}{result}{space 2}        1{col 35}{space 3}     0.21{col 47}{space 2}   0.6440
{txt}{space 9}(2 vs 0) 10  {res}{col 23}{text}{c |}{result}{space 2}        1{col 35}{space 3}     0.50{col 47}{space 2}   0.4781
{txt}{space 9}(2 vs 0) 11  {res}{col 23}{text}{c |}{result}{space 2}        1{col 35}{space 3}     0.93{col 47}{space 2}   0.3340
{txt}{space 9}(2 vs 0) 12  {res}{col 23}{text}{c |}{result}{space 2}        1{col 35}{space 3}     1.51{col 47}{space 2}   0.2189
{col 1}{text}               Joint {col 23}{c |}{result}  (not testable)
{col 1}{text}{hline 22}{c BT}{hline 11}{hline 12}{hline 11}
{res}
{txt}{hline 22}{c TT}{hline 11}{hline 11}{hline 14}{hline 12}
{col 23}{c |}{col 35} Delta-method
{col 23}{c |}   Contrast{col 35}   std. err.{col 47}     [95% con{col 60}f. interval]
{hline 22}{c +}{hline 11}{hline 11}{hline 14}{hline 12}
t1_interstate_cat@_at {c |}
{space 9}(2 vs 0)  1  {c |}{col 23}{res}{space 2} .0024659{col 35}{space 2} .0036698{col 46}{space 5}-.0047268{col 60}{space 3} .0096586
{txt}{space 9}(2 vs 0)  2  {c |}{col 23}{res}{space 2} .0024892{col 35}{space 2} .0035418{col 46}{space 5}-.0044526{col 60}{space 3}  .009431
{txt}{space 9}(2 vs 0)  3  {c |}{col 23}{res}{space 2} .0024831{col 35}{space 2} .0031621{col 46}{space 5}-.0037145{col 60}{space 3} .0086806
{txt}{space 9}(2 vs 0)  4  {c |}{col 23}{res}{space 2} .0022224{col 35}{space 2} .0032033{col 46}{space 5}-.0040559{col 60}{space 3} .0085008
{txt}{space 9}(2 vs 0)  5  {c |}{col 23}{res}{space 2} .0017112{col 35}{space 2} .0035511{col 46}{space 5}-.0052488{col 60}{space 3} .0086712
{txt}{space 9}(2 vs 0)  6  {c |}{col 23}{res}{space 2} .0009766{col 35}{space 2} .0039833{col 46}{space 5}-.0068305{col 60}{space 3} .0087836
{txt}{space 9}(2 vs 0)  7  {c |}{col 23}{res}{space 2} .0000457{col 35}{space 2} .0043863{col 46}{space 5}-.0085514{col 60}{space 3} .0086427
{txt}{space 9}(2 vs 0)  8  {c |}{col 23}{res}{space 2}-.0010543{col 35}{space 2} .0047177{col 46}{space 5}-.0103007{col 60}{space 3} .0081921
{txt}{space 9}(2 vs 0)  9  {c |}{col 23}{res}{space 2}-.0022961{col 35}{space 2} .0049691{col 46}{space 5}-.0120354{col 60}{space 3} .0074432
{txt}{space 9}(2 vs 0) 10  {c |}{col 23}{res}{space 2}-.0036526{col 35}{space 2} .0051492{col 46}{space 5}-.0137449{col 60}{space 3} .0064397
{txt}{space 9}(2 vs 0) 11  {c |}{col 23}{res}{space 2}-.0050966{col 35}{space 2} .0052751{col 46}{space 5}-.0154357{col 60}{space 3} .0052425
{txt}{space 9}(2 vs 0) 12  {c |}{col 23}{res}{space 2}-.0066009{col 35}{space 2} .0053687{col 46}{space 5}-.0171233{col 60}{space 3} .0039215
{txt}{hline 22}{c BT}{hline 11}{hline 11}{hline 14}{hline 12}
{res}{txt}
{com}. 
. marginsplot, recast(connected) ciopt(color(%40)) recastci(rarea) /// 
> yline(0, lcolor(%40gs) lpattern(shortdash)) ///
> legend(on order(1 "High Task Complexity - Low Task Complexity") pos(6) ring(2) cols(2) size(10pt))  ///
> title(" {c -(}bf:FIGURE B2B{c )-}""{c -(}bf:Conditional Adaptation Marginal Effect By Task Complexity{c )-}" "{c -(}bf:(Interstate Claims: Absolute Type I Program Error Rate [MODEL B2]){c )-}", size(10pt) linegap(0.7) margin(t+1 b+1 r-6)) ///
> xtitle("Months since IT Reform", size(10pt) margin(t+2 b+2)) ///
> ytitle("Absolute Type I Program Error Rate", size(10pt) margin(r+2)) ///
> xlabel(0 "0" 1 "1" 6 "6" 12 "12" 18 "18" 24 "24" 30 "30" 36 "36" 42 "42" 48 "48" 54 "54" 60 "60", labsize(9pt) ) ///
> ylabel(, labsize(9pt) format(%9.2f) angle(0)) xsize(6)
{res}
{text}{p 0 0 2}Variables that uniquely identify margins: {bf:itmod_monthcount}{p_end}
{p 0 4 2}
{txt}(note:  named style
% 40gs not found in class
color,  default attributes used)
{p_end}
{res}{txt}
{com}. *
. *
. *
. graph save "Graph" "C:\Users\gk57526\Dropbox\Administration of UI Programs in the American States (Ji-Hyeun Hong)\Performance Management Project (Paper #3)\EMPIRICS\GRAPHICS\marginsplot.Model B2.FIGURE B2B.04-10-2025.gph", replace
{txt}{p 0 4 2}
(file {bf}
C:\Users\gk57526\Dropbox\Administration of UI Programs in the American States (Ji-Hyeun Hong)\Performance Management Project (Paper #3)\EMPIRICS\GRAPHICS\marginsplot.Model B2.FIGURE B2B.04-10-2025.gph{rm}
not found)
{p_end}
{res}{txt}file {bf:C:\Users\gk57526\Dropbox\Administration of UI Programs in the American States (Ji-Hyeun Hong)\Performance Management Project (Paper #3)\EMPIRICS\GRAPHICS\marginsplot.Model B2.FIGURE B2B.04-10-2025.gph} saved

{com}. *
. *
. *
. *
. * [MODEL B2: ABSOLUTE TYPE I ERROR RATE] FIGURE B2C: MARGINAL DIFFERENTIAL EFFECT BETWEEN HIGH TASK COMPLEXITY (t1_diffoccupseek_cat==2) & LOW COMPLEXITY (t1_diffoccupseek_cat==0) VALUES [WITH RESPECT TO MONTHS SINCE ADOPTION (t + k) : 0 1 6 12.....60]: ***
. 
. margins r.t1_diffoccupseek_cat if t1_diffoccupseek_cat==0|t1_diffoccupseek_cat==2, at(itmod_monthcount=(0 1 6 12 18 24 30 36 42 48 54 60))
{res}
{txt}{col 1}Contrasts of predictive margins{col 58}{lalign 13:Number of obs}{col 71} = {res}{ralign 5:3,688}
{txt}{col 1}Model VCE: {res:Bootstrap}

{txt}{p2colset 1 13 13 2}{...}
{p2col:Expression:}{res:Mean function, predict()}{p_end}
{p2colreset}{...}
{lalign 8:1._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:0}}
{lalign 8:2._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:1}}
{lalign 8:3._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:6}}
{lalign 8:4._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:12}}
{lalign 8:5._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:18}}
{lalign 8:6._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:24}}
{lalign 8:7._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:30}}
{lalign 8:8._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:36}}
{lalign 8:9._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:42}}
{lalign 8:10._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:48}}
{lalign 8:11._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:54}}
{lalign 8:12._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:60}}
{res}
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 12}{hline 11}
{col 26}{text}{c |}         df{col 38}        chi2{col 50}     P>chi2
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 12}{hline 11}
t1_diffoccupseek_cat@_at {c |}
{space 12}(2 vs 0)  1  {res}{col 26}{text}{c |}{result}{space 2}        1{col 38}{space 3}    17.29{col 50}{space 2}   0.0000
{txt}{space 12}(2 vs 0)  2  {res}{col 26}{text}{c |}{result}{space 2}        1{col 38}{space 3}    17.75{col 50}{space 2}   0.0000
{txt}{space 12}(2 vs 0)  3  {res}{col 26}{text}{c |}{result}{space 2}        1{col 38}{space 3}    17.43{col 50}{space 2}   0.0000
{txt}{space 12}(2 vs 0)  4  {res}{col 26}{text}{c |}{result}{space 2}        1{col 38}{space 3}    12.84{col 50}{space 2}   0.0003
{txt}{space 12}(2 vs 0)  5  {res}{col 26}{text}{c |}{result}{space 2}        1{col 38}{space 3}     8.44{col 50}{space 2}   0.0037
{txt}{space 12}(2 vs 0)  6  {res}{col 26}{text}{c |}{result}{space 2}        1{col 38}{space 3}     5.68{col 50}{space 2}   0.0171
{txt}{space 12}(2 vs 0)  7  {res}{col 26}{text}{c |}{result}{space 2}        1{col 38}{space 3}     4.09{col 50}{space 2}   0.0431
{txt}{space 12}(2 vs 0)  8  {res}{col 26}{text}{c |}{result}{space 2}        1{col 38}{space 3}     3.16{col 50}{space 2}   0.0754
{txt}{space 12}(2 vs 0)  9  {res}{col 26}{text}{c |}{result}{space 2}        1{col 38}{space 3}     2.61{col 50}{space 2}   0.1065
{txt}{space 12}(2 vs 0) 10  {res}{col 26}{text}{c |}{result}{space 2}        1{col 38}{space 3}     2.27{col 50}{space 2}   0.1317
{txt}{space 12}(2 vs 0) 11  {res}{col 26}{text}{c |}{result}{space 2}        1{col 38}{space 3}     2.08{col 50}{space 2}   0.1497
{txt}{space 12}(2 vs 0) 12  {res}{col 26}{text}{c |}{result}{space 2}        1{col 38}{space 3}     1.97{col 50}{space 2}   0.1609
{col 1}{text}                  Joint {col 26}{c |}{result}  (not testable)
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 12}{hline 11}
{res}
{txt}{hline 25}{c TT}{hline 11}{hline 11}{hline 14}{hline 12}
{col 26}{c |}{col 38} Delta-method
{col 26}{c |}   Contrast{col 38}   std. err.{col 50}     [95% con{col 63}f. interval]
{hline 25}{c +}{hline 11}{hline 11}{hline 14}{hline 12}
t1_diffoccupseek_cat@_at {c |}
{space 12}(2 vs 0)  1  {c |}{col 26}{res}{space 2}  .014138{col 38}{space 2} .0034001{col 49}{space 5}  .007474{col 63}{space 3}  .020802
{txt}{space 12}(2 vs 0)  2  {c |}{col 26}{res}{space 2}  .013904{col 38}{space 2} .0032999{col 49}{space 5} .0074362{col 63}{space 3} .0203717
{txt}{space 12}(2 vs 0)  3  {c |}{col 26}{res}{space 2} .0128092{col 38}{space 2}  .003068{col 49}{space 5} .0067961{col 63}{space 3} .0188224
{txt}{space 12}(2 vs 0)  4  {c |}{col 26}{res}{space 2} .0116557{col 38}{space 2} .0032525{col 49}{space 5}  .005281{col 63}{space 3} .0180305
{txt}{space 12}(2 vs 0)  5  {c |}{col 26}{res}{space 2} .0106679{col 38}{space 2} .0036723{col 49}{space 5} .0034704{col 63}{space 3} .0178654
{txt}{space 12}(2 vs 0)  6  {c |}{col 26}{res}{space 2} .0098362{col 38}{space 2}  .004126{col 49}{space 5} .0017494{col 63}{space 3}  .017923
{txt}{space 12}(2 vs 0)  7  {c |}{col 26}{res}{space 2}  .009151{col 38}{space 2} .0045245{col 49}{space 5} .0002831{col 63}{space 3} .0180189
{txt}{space 12}(2 vs 0)  8  {c |}{col 26}{res}{space 2} .0086027{col 38}{space 2}  .004839{col 49}{space 5}-.0008815{col 63}{space 3}  .018087
{txt}{space 12}(2 vs 0)  9  {c |}{col 26}{res}{space 2} .0081818{col 38}{space 2} .0050687{col 49}{space 5}-.0017528{col 63}{space 3} .0181163
{txt}{space 12}(2 vs 0) 10  {c |}{col 26}{res}{space 2} .0078785{col 38}{space 2} .0052266{col 49}{space 5}-.0023654{col 63}{space 3} .0181225
{txt}{space 12}(2 vs 0) 11  {c |}{col 26}{res}{space 2} .0076835{col 38}{space 2} .0053329{col 49}{space 5}-.0027688{col 63}{space 3} .0181358
{txt}{space 12}(2 vs 0) 12  {c |}{col 26}{res}{space 2}  .007587{col 38}{space 2} .0054118{col 49}{space 5}  -.00302{col 63}{space 3} .0181939
{txt}{hline 25}{c BT}{hline 11}{hline 11}{hline 14}{hline 12}
{res}{txt}
{com}. 
. marginsplot, recast(connected) ciopt(color(%40)) recastci(rarea) /// 
> yline(0, lcolor(%40gs) lpattern(shortdash)) ///
> legend(on order(1 "High Task Complexity - Low Task Complexity") pos(6) ring(2) cols(2) size(10pt))  ///
> title(" {c -(}bf:FIGURE B2C{c )-}""{c -(}bf:Conditional Adaptation Marginal Effect By Task Complexity{c )-}" "{c -(}bf:(Seeking Different Occupation: Absolute Type I Program Error Rate [MODEL B2]){c )-}", size(10pt) linegap(0.7) margin(t+1 b+1 r-6)) ///
> xtitle("Months since IT Reform", size(10pt) margin(t+2 b+2)) ///
> ytitle("Absolute Type I Program Error Rate", size(10pt) margin(r+2)) ///
> xlabel(0 "0" 1 "1" 6 "6" 12 "12" 18 "18" 24 "24" 30 "30" 36 "36" 42 "42" 48 "48" 54 "54" 60 "60", labsize(9pt) ) ///
> ylabel(#3, labsize(9pt) format(%9.2f) angle(0)) xsize(6)
{res}
{text}{p 0 0 2}Variables that uniquely identify margins: {bf:itmod_monthcount}{p_end}
{p 0 4 2}
{txt}(note:  named style
% 40gs not found in class
color,  default attributes used)
{p_end}
{res}{txt}
{com}. *
. *
. *
. graph save "Graph" "C:\Users\gk57526\Dropbox\Administration of UI Programs in the American States (Ji-Hyeun Hong)\Performance Management Project (Paper #3)\EMPIRICS\GRAPHICS\marginsplot.Model B2.FIGURE B2C.04-10-2025.gph", replace
{txt}{p 0 4 2}
(file {bf}
C:\Users\gk57526\Dropbox\Administration of UI Programs in the American States (Ji-Hyeun Hong)\Performance Management Project (Paper #3)\EMPIRICS\GRAPHICS\marginsplot.Model B2.FIGURE B2C.04-10-2025.gph{rm}
not found)
{p_end}
{res}{txt}file {bf:C:\Users\gk57526\Dropbox\Administration of UI Programs in the American States (Ji-Hyeun Hong)\Performance Management Project (Paper #3)\EMPIRICS\GRAPHICS\marginsplot.Model B2.FIGURE B2C.04-10-2025.gph} saved

{com}. 
. 
. 
. *********************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************
. 
. 
. 
. 
. 
. *** TESTING H2 & H4: RELATIVE TYPE I ERROR RATE [TYPE I ERROR RATE / (TYPE I ERROR RATE + TYPE II ERROR RATE)] ORGANIZATIONAL ADAPTATION ***
. 
. 
. 
. 
. *** ESTIMATE MODEL B3: RELATIVE TYPE I ERROR RATE [MODEL 3 omitting ADDITIONAL COVARIATES: PROPORTION OF SAMPLE-WEIGHTED CASES OF TOTAL ERRORS VIA WEEKLY BAM SURVEY AGGREGATED TO MONTHLY OBSERVATIONS: [ONLY STATE, YEAR, AND YEAR-ADOPTION COHORT*TREATMENT UNIT EFFECTS] ***  (FIGURES B2D-B2F) 
. 
. 
. 
. npregress series  relt1error_rat  itmod_monthcount  i.relt1_interstate_cat i.relt1_diffoccupseek_cat  if itmod_adopt_state==1, asis(i.stateid i.year  adoptcohort_2002_itadopt  adoptcohort_2004_itadopt  adoptcohort_2006_itadopt adoptcohort_2007_itadopt   adoptcohort_2009_itadopt  adoptcohort_2010_itadopt  adoptcohort_2013_itadopt  adoptcohort_2014_itadopt  adoptcohort_2015_itadopt  adoptcohort_2016_itadopt  adoptcohort_2017_itadopt  adoptcohort_2018_itadopt  adoptcohort_2020_itadopt  adoptcohort_2021_itadopt)  vce(bootstrap, seed(123) rep(1000))
{res}{txt}(running {bf:npregress} on estimation sample)
{res}
{text}Bootstrap replications ({result:1,000}){text}: 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{txt}{space 24} {c |}
{space 20}year {c |}
{space 19}2003  {c |}{col 26}{res}{space 2}-.0013539{col 38}{space 2} .0205768{col 49}{space 1}   -0.07{col 58}{space 3}0.948{col 66}{space 4}-.0416837{col 79}{space 3}  .038976
{txt}{space 19}2004  {c |}{col 26}{res}{space 2}-.0491145{col 38}{space 2}  .019849{col 49}{space 1}   -2.47{col 58}{space 3}0.013{col 66}{space 4}-.0880178{col 79}{space 3}-.0102111
{txt}{space 19}2005  {c |}{col 26}{res}{space 2}-.0308185{col 38}{space 2} .0218005{col 49}{space 1}   -1.41{col 58}{space 3}0.157{col 66}{space 4}-.0735468{col 79}{space 3} .0119098
{txt}{space 19}2006  {c |}{col 26}{res}{space 2}-.0449201{col 38}{space 2}  .021029{col 49}{space 1}   -2.14{col 58}{space 3}0.033{col 66}{space 4}-.0861361{col 79}{space 3}-.0037041
{txt}{space 19}2007  {c |}{col 26}{res}{space 2}-.0576082{col 38}{space 2} .0209379{col 49}{space 1}   -2.75{col 58}{space 3}0.006{col 66}{space 4}-.0986458{col 79}{space 3}-.0165706
{txt}{space 19}2008  {c |}{col 26}{res}{space 2}-.0624579{col 38}{space 2} .0210867{col 49}{space 1}   -2.96{col 58}{space 3}0.003{col 66}{space 4}-.1037871{col 79}{space 3}-.0211287
{txt}{space 19}2009  {c |}{col 26}{res}{space 2}-.0161506{col 38}{space 2}  .021205{col 49}{space 1}   -0.76{col 58}{space 3}0.446{col 66}{space 4}-.0577116{col 79}{space 3} .0254104
{txt}{space 19}2010  {c |}{col 26}{res}{space 2} -.006082{col 38}{space 2}  .019832{col 49}{space 1}   -0.31{col 58}{space 3}0.759{col 66}{space 4}-.0449519{col 79}{space 3}  .032788
{txt}{space 19}2011  {c |}{col 26}{res}{space 2}-.0289257{col 38}{space 2} .0202259{col 49}{space 1}   -1.43{col 58}{space 3}0.153{col 66}{space 4}-.0685677{col 79}{space 3} .0107163
{txt}{space 19}2012  {c |}{col 26}{res}{space 2}-.0006462{col 38}{space 2} .0204419{col 49}{space 1}   -0.03{col 58}{space 3}0.975{col 66}{space 4}-.0407116{col 79}{space 3} .0394191
{txt}{space 19}2013  {c |}{col 26}{res}{space 2}-.0569736{col 38}{space 2}  .020957{col 49}{space 1}   -2.72{col 58}{space 3}0.007{col 66}{space 4}-.0980486{col 79}{space 3}-.0158986
{txt}{space 19}2014  {c |}{col 26}{res}{space 2}-.0439331{col 38}{space 2} .0218673{col 49}{space 1}   -2.01{col 58}{space 3}0.045{col 66}{space 4}-.0867923{col 79}{space 3}-.0010739
{txt}{space 19}2015  {c |}{col 26}{res}{space 2}-.0127692{col 38}{space 2} .0232106{col 49}{space 1}   -0.55{col 58}{space 3}0.582{col 66}{space 4} -.058261{col 79}{space 3} .0327227
{txt}{space 19}2016  {c |}{col 26}{res}{space 2}-.0184055{col 38}{space 2} .0227706{col 49}{space 1}   -0.81{col 58}{space 3}0.419{col 66}{space 4} -.063035{col 79}{space 3} .0262239
{txt}{space 19}2017  {c |}{col 26}{res}{space 2}-.0058047{col 38}{space 2} .0241571{col 49}{space 1}   -0.24{col 58}{space 3}0.810{col 66}{space 4}-.0531517{col 79}{space 3} .0415424
{txt}{space 19}2018  {c |}{col 26}{res}{space 2}-.0074335{col 38}{space 2} .0241086{col 49}{space 1}   -0.31{col 58}{space 3}0.758{col 66}{space 4}-.0546855{col 79}{space 3} .0398184
{txt}{space 19}2019  {c |}{col 26}{res}{space 2}-.0396947{col 38}{space 2} .0259515{col 49}{space 1}   -1.53{col 58}{space 3}0.126{col 66}{space 4}-.0905588{col 79}{space 3} .0111693
{txt}{space 19}2020  {c |}{col 26}{res}{space 2} .0846189{col 38}{space 2} .0338676{col 49}{space 1}    2.50{col 58}{space 3}0.012{col 66}{space 4} .0182396{col 79}{space 3} .1509981
{txt}{space 19}2021  {c |}{col 26}{res}{space 2} .2471908{col 38}{space 2} .0320579{col 49}{space 1}    7.71{col 58}{space 3}0.000{col 66}{space 4} .1843585{col 79}{space 3} .3100231
{txt}{space 19}2022  {c |}{col 26}{res}{space 2} .1310071{col 38}{space 2} .0375688{col 49}{space 1}    3.49{col 58}{space 3}0.000{col 66}{space 4} .0573737{col 79}{space 3} .2046406
{txt}{space 24} {c |}
adoptcohort_2002_itadopt {c |}{col 26}{res}{space 2} .2213118{col 38}{space 2} .1482245{col 49}{space 1}    1.49{col 58}{space 3}0.135{col 66}{space 4}-.0692029{col 79}{space 3} .5118266
{txt}adoptcohort_2004_itadopt {c |}{col 26}{res}{space 2} .1949482{col 38}{space 2} .1146325{col 49}{space 1}    1.70{col 58}{space 3}0.089{col 66}{space 4}-.0297275{col 79}{space 3} .4196238
{txt}adoptcohort_2006_itadopt {c |}{col 26}{res}{space 2} .2302886{col 38}{space 2} .1191436{col 49}{space 1}    1.93{col 58}{space 3}0.053{col 66}{space 4}-.0032286{col 79}{space 3} .4638057
{txt}adoptcohort_2007_itadopt {c |}{col 26}{res}{space 2} .0324789{col 38}{space 2} .1099879{col 49}{space 1}    0.30{col 58}{space 3}0.768{col 66}{space 4}-.1830935{col 79}{space 3} .2480513
{txt}adoptcohort_2009_itadopt {c |}{col 26}{res}{space 2}-.0659123{col 38}{space 2} .1100021{col 49}{space 1}   -0.60{col 58}{space 3}0.549{col 66}{space 4}-.2815126{col 79}{space 3} .1496879
{txt}adoptcohort_2010_itadopt {c |}{col 26}{res}{space 2}-.0724534{col 38}{space 2}  .110565{col 49}{space 1}   -0.66{col 58}{space 3}0.512{col 66}{space 4}-.2891569{col 79}{space 3} .1442501
{txt}adoptcohort_2013_itadopt {c |}{col 26}{res}{space 2} .0343924{col 38}{space 2}  .105769{col 49}{space 1}    0.33{col 58}{space 3}0.745{col 66}{space 4}-.1729111{col 79}{space 3} .2416958
{txt}adoptcohort_2014_itadopt {c |}{col 26}{res}{space 2} .0159548{col 38}{space 2} .1098284{col 49}{space 1}    0.15{col 58}{space 3}0.884{col 66}{space 4}-.1993048{col 79}{space 3} .2312145
{txt}adoptcohort_2015_itadopt {c |}{col 26}{res}{space 2}-.0533693{col 38}{space 2} .1054058{col 49}{space 1}   -0.51{col 58}{space 3}0.613{col 66}{space 4}-.2599609{col 79}{space 3} .1532223
{txt}adoptcohort_2016_itadopt {c |}{col 26}{res}{space 2}  .063917{col 38}{space 2} .1083256{col 49}{space 1}    0.59{col 58}{space 3}0.555{col 66}{space 4}-.1483972{col 79}{space 3} .2762312
{txt}adoptcohort_2017_itadopt {c |}{col 26}{res}{space 2}-.0018299{col 38}{space 2} .1004476{col 49}{space 1}   -0.02{col 58}{space 3}0.985{col 66}{space 4}-.1987037{col 79}{space 3} .1950438
{txt}adoptcohort_2018_itadopt {c |}{col 26}{res}{space 2}-.0613413{col 38}{space 2} .1042069{col 49}{space 1}   -0.59{col 58}{space 3}0.556{col 66}{space 4}-.2655831{col 79}{space 3} .1429004
{txt}adoptcohort_2020_itadopt {c |}{col 26}{res}{space 2} .0460056{col 38}{space 2} .1062313{col 49}{space 1}    0.43{col 58}{space 3}0.665{col 66}{space 4}-.1622039{col 79}{space 3}  .254215
{txt}adoptcohort_2021_itadopt {c |}{col 26}{res}{space 2}-.1425006{col 38}{space 2} .1059712{col 49}{space 1}   -1.34{col 58}{space 3}0.179{col 66}{space 4}-.3502004{col 79}{space 3} .0651992
{txt}{hline 25}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. 
. 
. ** COMPUTE PSEUDO R^2 [SSE / (SSE + SSR) = EXPLAINED/PREDICTED SUM OF SQUARES / (EXPLAINED/PREDICTED SUM OF SQUARES + RESIDUAL SUM OF SQUARES)] = SSE / SST
. 
. predict predsy_m3b if e(sample)
{txt}(statistic {bf:mean} assumed; mean function)
{res}{txt}
{com}. predict residsy_m3b if e(sample), residuals
{res}{txt}(5,780 missing values generated)

{com}. 
. gen sse_m3b = predsy_m3b * predsy_m3b if e(sample)
{txt}(5,780 missing values generated)

{com}. gen ssr_m3b = residsy_m3b * residsy_m3b if e(sample)
{txt}(5,780 missing values generated)

{com}. 
. egen sum_sse_m3b = total(sse_m3b) if e(sample)
{txt}(5,780 missing values generated)

{com}. egen sum_ssr_m3b = total(ssr_m3b) if e(sample)
{txt}(5,780 missing values generated)

{com}. 
. gen r2_m3b = sum_ssr_m3b/(sum_sse_m3b + sum_ssr_m3b)
{txt}(5,780 missing values generated)

{com}. 
. sum r2_m3b

{txt}    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 6}r2_m3b {c |}{res}      6,771    .3706604           0   .3706604   .3706604
{txt}
{com}. 
. *
. *
. *
. *
. * [MODEL B3: RELATIVE TYPE I ERROR RATE] FIGURE B2D:  UNCONDITIONAL ADAPTATION EFFECTS -- E(Y) [WITH RESPECT TO MONTHS SINCE ADOPTION (t + k) : 0 1 6 12.....60]
. 
. margins, at(itmod_monthcount=(0 1 6 12 18 24 30 36 42 48 54 60))
{res}
{txt}{col 1}Predictive margins{col 58}{lalign 13:Number of obs}{col 71} = {res}{ralign 5:6,771}
{txt}{col 1}Model VCE: {res:Bootstrap}

{txt}{p2colset 1 13 13 2}{...}
{p2col:Expression:}{res:Mean function, predict()}{p_end}
{p2colreset}{...}
{lalign 8:1._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:0}}
{lalign 8:2._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:1}}
{lalign 8:3._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:6}}
{lalign 8:4._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:12}}
{lalign 8:5._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:18}}
{lalign 8:6._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:24}}
{lalign 8:7._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:30}}
{lalign 8:8._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:36}}
{lalign 8:9._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:42}}
{lalign 8:10._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:48}}
{lalign 8:11._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:54}}
{lalign 8:12._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:60}}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}     Margin{col 26}   std. err.{col 38}      z{col 46}   P>|z|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}_at {c |}
{space 10}1  {c |}{col 14}{res}{space 2} .3111568{col 26}{space 2} .0072177{col 37}{space 1}   43.11{col 46}{space 3}0.000{col 54}{space 4} .2970103{col 67}{space 3} .3253032
{txt}{space 10}2  {c |}{col 14}{res}{space 2} .3091422{col 26}{space 2} .0066879{col 37}{space 1}   46.22{col 46}{space 3}0.000{col 54}{space 4} .2960342{col 67}{space 3} .3222501
{txt}{space 10}3  {c |}{col 14}{res}{space 2} .2996516{col 26}{space 2} .0048032{col 37}{space 1}   62.39{col 46}{space 3}0.000{col 54}{space 4} .2902376{col 67}{space 3} .3090657
{txt}{space 10}4  {c |}{col 14}{res}{space 2} .2894816{col 26}{space 2} .0047582{col 37}{space 1}   60.84{col 46}{space 3}0.000{col 54}{space 4} .2801557{col 67}{space 3} .2988074
{txt}{space 10}5  {c |}{col 14}{res}{space 2} .2805441{col 26}{space 2}   .00633{col 37}{space 1}   44.32{col 46}{space 3}0.000{col 54}{space 4} .2681375{col 67}{space 3} .2929506
{txt}{space 10}6  {c |}{col 14}{res}{space 2} .2727367{col 26}{space 2} .0082016{col 37}{space 1}   33.25{col 46}{space 3}0.000{col 54}{space 4} .2566618{col 67}{space 3} .2888115
{txt}{space 10}7  {c |}{col 14}{res}{space 2} .2659569{col 26}{space 2}  .009919{col 37}{space 1}   26.81{col 46}{space 3}0.000{col 54}{space 4}  .246516{col 67}{space 3} .2853979
{txt}{space 10}8  {c |}{col 14}{res}{space 2} .2601024{col 26}{space 2} .0113782{col 37}{space 1}   22.86{col 46}{space 3}0.000{col 54}{space 4} .2378015{col 67}{space 3} .2824032
{txt}{space 10}9  {c |}{col 14}{res}{space 2} .2550706{col 26}{space 2} .0125721{col 37}{space 1}   20.29{col 46}{space 3}0.000{col 54}{space 4} .2304297{col 67}{space 3} .2797114
{txt}{space 9}10  {c |}{col 14}{res}{space 2}  .250759{col 26}{space 2}  .013526{col 37}{space 1}   18.54{col 46}{space 3}0.000{col 54}{space 4} .2242485{col 67}{space 3} .2772696
{txt}{space 9}11  {c |}{col 14}{res}{space 2} .2470653{col 26}{space 2} .0142785{col 37}{space 1}   17.30{col 46}{space 3}0.000{col 54}{space 4}   .21908{col 67}{space 3} .2750506
{txt}{space 9}12  {c |}{col 14}{res}{space 2} .2438869{col 26}{space 2} .0148737{col 37}{space 1}   16.40{col 46}{space 3}0.000{col 54}{space 4}  .214735{col 67}{space 3} .2730388
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. marginsplot, recast(connected) ciopt(color(%40)) recastci(rarea) ///
> legend(on order(1 "Unconditional Adaptation") pos(6) ring(2) cols(2) size(9pt))  ///
> title(" {c -(}bf:FIGURE B2D{c )-}""{c -(}bf:Unconditional Adaptation Effect{c )-}" "{c -(}bf:(Relative Type I Program Error Rate [MODEL B3]){c )-}", size(10pt) linegap(0.7) margin(t+1 b+2 r-6)) ///
> xtitle("Months since IT Reform", size(10pt) margin(t+2 b+2)) ///
> ytitle("Relative Type I Program Error Rate", size(10pt) margin(r+2)) ///
> xlabel(0 "0" 1 "1" 6 "6" 12 "12" 18 "18" 24 "24" 30 "30" 36 "36" 42 "42" 48 "48" 54 "54" 60 "60", labsize(9pt) ) ///
> ylabel(, labsize(9pt) format(%9.2f) angle(0)) xsize(6)
{res}
{text}{p 0 0 2}Variables that uniquely identify margins: {bf:itmod_monthcount}{p_end}
{res}{txt}
{com}. *
. *
. graph save "Graph" "C:\Users\gk57526\Dropbox\Administration of UI Programs in the American States (Ji-Hyeun Hong)\Performance Management Project (Paper #3)\EMPIRICS\GRAPHICS\marginsplot.Model B3.FIGURE B2D.04-10-2025.gph", replace
{txt}{p 0 4 2}
(file {bf}
C:\Users\gk57526\Dropbox\Administration of UI Programs in the American States (Ji-Hyeun Hong)\Performance Management Project (Paper #3)\EMPIRICS\GRAPHICS\marginsplot.Model B3.FIGURE B2D.04-10-2025.gph{rm}
not found)
{p_end}
{res}{txt}file {bf:C:\Users\gk57526\Dropbox\Administration of UI Programs in the American States (Ji-Hyeun Hong)\Performance Management Project (Paper #3)\EMPIRICS\GRAPHICS\marginsplot.Model B3.FIGURE B2D.04-10-2025.gph} saved

{com}. 
. *
. *
. * [MODEL B3: RELATIVE TYPE I ERROR RATE] FIGURE B2E:  MARGINAL DIFFERENTIAL EFFECT BETWEEN HIGH TASK COMPLEXITY (relt1_interstate_cat==2) & LOW COMPLEXITY (relt1_interstate_cat==0) VALUES [WITH RESPECT TO MONTHS SINCE ADOPTION (t + k) : 0 1 6 12.....60]: ***
. 
. margins r.relt1_interstate_cat if relt1_interstate_cat==0|relt1_interstate_cat==2, at(itmod_monthcount=(0 1 6 12 18 24 30 36 42 48 54 60))
{res}
{txt}{col 1}Contrasts of predictive margins{col 58}{lalign 13:Number of obs}{col 71} = {res}{ralign 5:3,458}
{txt}{col 1}Model VCE: {res:Bootstrap}

{txt}{p2colset 1 13 13 2}{...}
{p2col:Expression:}{res:Mean function, predict()}{p_end}
{p2colreset}{...}
{lalign 8:1._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:0}}
{lalign 8:2._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:1}}
{lalign 8:3._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:6}}
{lalign 8:4._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:12}}
{lalign 8:5._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:18}}
{lalign 8:6._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:24}}
{lalign 8:7._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:30}}
{lalign 8:8._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:36}}
{lalign 8:9._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:42}}
{lalign 8:10._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:48}}
{lalign 8:11._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:54}}
{lalign 8:12._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:60}}
{res}
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 12}{hline 11}
{col 26}{text}{c |}         df{col 38}        chi2{col 50}     P>chi2
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 12}{hline 11}
relt1_interstate_cat@_at {c |}
{space 12}(2 vs 0)  1  {res}{col 26}{text}{c |}{result}{space 2}        1{col 38}{space 3}     2.56{col 50}{space 2}   0.1097
{txt}{space 12}(2 vs 0)  2  {res}{col 26}{text}{c |}{result}{space 2}        1{col 38}{space 3}     2.87{col 50}{space 2}   0.0905
{txt}{space 12}(2 vs 0)  3  {res}{col 26}{text}{c |}{result}{space 2}        1{col 38}{space 3}     3.96{col 50}{space 2}   0.0465
{txt}{space 12}(2 vs 0)  4  {res}{col 26}{text}{c |}{result}{space 2}        1{col 38}{space 3}     3.89{col 50}{space 2}   0.0485
{txt}{space 12}(2 vs 0)  5  {res}{col 26}{text}{c |}{result}{space 2}        1{col 38}{space 3}     3.32{col 50}{space 2}   0.0685
{txt}{space 12}(2 vs 0)  6  {res}{col 26}{text}{c |}{result}{space 2}        1{col 38}{space 3}     2.84{col 50}{space 2}   0.0917
{txt}{space 12}(2 vs 0)  7  {res}{col 26}{text}{c |}{result}{space 2}        1{col 38}{space 3}     2.52{col 50}{space 2}   0.1123
{txt}{space 12}(2 vs 0)  8  {res}{col 26}{text}{c |}{result}{space 2}        1{col 38}{space 3}     2.30{col 50}{space 2}   0.1292
{txt}{space 12}(2 vs 0)  9  {res}{col 26}{text}{c |}{result}{space 2}        1{col 38}{space 3}     2.14{col 50}{space 2}   0.1436
{txt}{space 12}(2 vs 0) 10  {res}{col 26}{text}{c |}{result}{space 2}        1{col 38}{space 3}     2.00{col 50}{space 2}   0.1577
{txt}{space 12}(2 vs 0) 11  {res}{col 26}{text}{c |}{result}{space 2}        1{col 38}{space 3}     1.84{col 50}{space 2}   0.1744
{txt}{space 12}(2 vs 0) 12  {res}{col 26}{text}{c |}{result}{space 2}        1{col 38}{space 3}     1.66{col 50}{space 2}   0.1976
{col 1}{text}                  Joint {col 26}{c |}{result}  (not testable)
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 12}{hline 11}
{res}
{txt}{hline 25}{c TT}{hline 11}{hline 11}{hline 14}{hline 12}
{col 26}{c |}{col 38} Delta-method
{col 26}{c |}   Contrast{col 38}   std. err.{col 50}     [95% con{col 63}f. interval]
{hline 25}{c +}{hline 11}{hline 11}{hline 14}{hline 12}
relt1_interstate_cat@_at {c |}
{space 12}(2 vs 0)  1  {c |}{col 26}{res}{space 2}-.0203061{col 38}{space 2} .0126962{col 49}{space 5}-.0451902{col 63}{space 3}  .004578
{txt}{space 12}(2 vs 0)  2  {c |}{col 26}{res}{space 2}-.0208147{col 38}{space 2} .0122968{col 49}{space 5} -.044916{col 63}{space 3} .0032866
{txt}{space 12}(2 vs 0)  3  {c |}{col 26}{res}{space 2}-.0231368{col 38}{space 2} .0116234{col 49}{space 5}-.0459182{col 63}{space 3}-.0003554
{txt}{space 12}(2 vs 0)  4  {c |}{col 26}{res}{space 2}-.0254439{col 38}{space 2} .0128937{col 49}{space 5}-.0507151{col 63}{space 3}-.0001727
{txt}{space 12}(2 vs 0)  5  {c |}{col 26}{res}{space 2}-.0272384{col 38}{space 2} .0149546{col 49}{space 5}-.0565488{col 63}{space 3}  .002072
{txt}{space 12}(2 vs 0)  6  {c |}{col 26}{res}{space 2}-.0285311{col 38}{space 2}  .016919{col 49}{space 5}-.0616917{col 63}{space 3} .0046294
{txt}{space 12}(2 vs 0)  7  {c |}{col 26}{res}{space 2}-.0293331{col 38}{space 2} .0184712{col 49}{space 5} -.065536{col 63}{space 3} .0068697
{txt}{space 12}(2 vs 0)  8  {c |}{col 26}{res}{space 2}-.0296553{col 38}{space 2} .0195441{col 49}{space 5} -.067961{col 63}{space 3} .0086505
{txt}{space 12}(2 vs 0)  9  {c |}{col 26}{res}{space 2}-.0295084{col 38}{space 2} .0201758{col 49}{space 5}-.0690523{col 63}{space 3} .0100355
{txt}{space 12}(2 vs 0) 10  {c |}{col 26}{res}{space 2}-.0289036{col 38}{space 2} .0204562{col 49}{space 5}-.0689969{col 63}{space 3} .0111897
{txt}{space 12}(2 vs 0) 11  {c |}{col 26}{res}{space 2}-.0278517{col 38}{space 2} .0205053{col 49}{space 5}-.0680413{col 63}{space 3}  .012338
{txt}{space 12}(2 vs 0) 12  {c |}{col 26}{res}{space 2}-.0263635{col 38}{space 2} .0204623{col 49}{space 5}-.0664689{col 63}{space 3} .0137418
{txt}{hline 25}{c BT}{hline 11}{hline 11}{hline 14}{hline 12}
{res}{txt}
{com}. 
. marginsplot, recast(connected) ciopt(color(%40)) recastci(rarea) /// 
> yline(0, lcolor(%40gs) lpattern(shortdash)) ///
> legend(on order(1 "High Task Complexity - Low Task Complexity") pos(6) ring(2) cols(2) size(10pt))  ///
> title(" {c -(}bf:FIGURE B2E{c )-}""{c -(}bf:Conditional Adaptation Marginal Effect By Task Complexity{c )-}" "{c -(}bf:(Interstate Claims: Relative Type I Program Error Rate [MODEL B3]){c )-}", size(10pt) linegap(0.7) margin(t+1 b+1 r-6)) ///
> xtitle("Months since IT Reform", size(10pt) margin(t+2 b+2)) ///
> ytitle("Relative Type I Program Error Rate", size(10pt) margin(r+2)) ///
> xlabel(0 "0" 1 "1" 6 "6" 12 "12" 18 "18" 24 "24" 30 "30" 36 "36" 42 "42" 48 "48" 54 "54" 60 "60", labsize(9pt) ) ///
> ylabel(, labsize(9pt) format(%9.2f) angle(0)) xsize(6)
{res}
{text}{p 0 0 2}Variables that uniquely identify margins: {bf:itmod_monthcount}{p_end}
{p 0 4 2}
{txt}(note:  named style
% 40gs not found in class
color,  default attributes used)
{p_end}
{res}{txt}
{com}. *
. *
. *
. graph save "Graph" "C:\Users\gk57526\Dropbox\Administration of UI Programs in the American States (Ji-Hyeun Hong)\Performance Management Project (Paper #3)\EMPIRICS\GRAPHICS\marginsplot.Model B3.FIGURE B2E.04-10-2025.gph", replace
{txt}{p 0 4 2}
(file {bf}
C:\Users\gk57526\Dropbox\Administration of UI Programs in the American States (Ji-Hyeun Hong)\Performance Management Project (Paper #3)\EMPIRICS\GRAPHICS\marginsplot.Model B3.FIGURE B2E.04-10-2025.gph{rm}
not found)
{p_end}
{res}{txt}file {bf:C:\Users\gk57526\Dropbox\Administration of UI Programs in the American States (Ji-Hyeun Hong)\Performance Management Project (Paper #3)\EMPIRICS\GRAPHICS\marginsplot.Model B3.FIGURE B2E.04-10-2025.gph} saved

{com}. *
. *
. *
. *
. * [MODEL B3: RELATIVE TYPE I ERROR RATE] FIGURE B2F:  MARGINAL DIFFERENTIAL EFFECT BETWEEN HIGH TASK COMPLEXITY (relt1_diffoccupseek_cat==2) & LOW COMPLEXITY (relt1_extbenefits_cat==0) VALUES [WITH RESPECT TO MONTHS SINCE ADOPTION (t + k) : 0 1 6 12.....60]: ***
. 
. margins r.relt1_diffoccupseek_cat if relt1_diffoccupseek_cat==0|relt1_diffoccupseek_cat==2, at(itmod_monthcount=(0 1 6 12 18 24 30 36 42 48 54 60))
{res}
{txt}{col 1}Contrasts of predictive margins{col 58}{lalign 13:Number of obs}{col 71} = {res}{ralign 5:3,392}
{txt}{col 1}Model VCE: {res:Bootstrap}

{txt}{p2colset 1 13 13 2}{...}
{p2col:Expression:}{res:Mean function, predict()}{p_end}
{p2colreset}{...}
{lalign 8:1._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:0}}
{lalign 8:2._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:1}}
{lalign 8:3._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:6}}
{lalign 8:4._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:12}}
{lalign 8:5._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:18}}
{lalign 8:6._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:24}}
{lalign 8:7._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:30}}
{lalign 8:8._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:36}}
{lalign 8:9._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:42}}
{lalign 8:10._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:48}}
{lalign 8:11._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:54}}
{lalign 8:12._at: }{space 0}{lalign 16:itmod_monthcount} = {res:{ralign 2:60}}
{res}
{col 1}{text}{hline 28}{c TT}{hline 11}{hline 12}{hline 11}
{col 29}{text}{c |}         df{col 41}        chi2{col 53}     P>chi2
{res}{col 1}{text}{hline 28}{c +}{hline 11}{hline 12}{hline 11}
relt1_diffoccupseek_cat@_at {c |}
{space 15}(2 vs 0)  1  {res}{col 29}{text}{c |}{result}{space 2}        1{col 41}{space 3}     5.61{col 53}{space 2}   0.0178
{txt}{space 15}(2 vs 0)  2  {res}{col 29}{text}{c |}{result}{space 2}        1{col 41}{space 3}     5.43{col 53}{space 2}   0.0198
{txt}{space 15}(2 vs 0)  3  {res}{col 29}{text}{c |}{result}{space 2}        1{col 41}{space 3}     3.39{col 53}{space 2}   0.0655
{txt}{space 15}(2 vs 0)  4  {res}{col 29}{text}{c |}{result}{space 2}        1{col 41}{space 3}     1.26{col 53}{space 2}   0.2615
{txt}{space 15}(2 vs 0)  5  {res}{col 29}{text}{c |}{result}{space 2}        1{col 41}{space 3}     0.45{col 53}{space 2}   0.5008
{txt}{space 15}(2 vs 0)  6  {res}{col 29}{text}{c |}{result}{space 2}        1{col 41}{space 3}     0.19{col 53}{space 2}   0.6594
{txt}{space 15}(2 vs 0)  7  {res}{col 29}{text}{c |}{result}{space 2}        1{col 41}{space 3}     0.11{col 53}{space 2}   0.7368
{txt}{space 15}(2 vs 0)  8  {res}{col 29}{text}{c |}{result}{space 2}        1{col 41}{space 3}     0.10{col 53}{space 2}   0.7551
{txt}{space 15}(2 vs 0)  9  {res}{col 29}{text}{c |}{result}{space 2}        1{col 41}{space 3}     0.12{col 53}{space 2}   0.7301
{txt}{space 15}(2 vs 0) 10  {res}{col 29}{text}{c |}{result}{space 2}        1{col 41}{space 3}     0.18{col 53}{space 2}   0.6720
{txt}{space 15}(2 vs 0) 11  {res}{col 29}{text}{c |}{result}{space 2}        1{col 41}{space 3}     0.29{col 53}{space 2}   0.5894
{txt}{space 15}(2 vs 0) 12  {res}{col 29}{text}{c |}{result}{space 2}        1{col 41}{space 3}     0.47{col 53}{space 2}   0.4925
{col 1}{text}                     Joint {col 29}{c |}{result}  (not testable)
{col 1}{text}{hline 28}{c BT}{hline 11}{hline 12}{hline 11}
{res}
{txt}{hline 28}{c TT}{hline 11}{hline 11}{hline 14}{hline 12}
{col 29}{c |}{col 41} Delta-method
{col 29}{c |}   Contrast{col 41}   std. err.{col 53}     [95% con{col 66}f. interval]
{hline 28}{c +}{hline 11}{hline 11}{hline 14}{hline 12}
relt1_diffoccupseek_cat@_at {c |}
{space 15}(2 vs 0)  1  {c |}{col 29}{res}{space 2} .0270082{col 41}{space 2} .0113988{col 52}{space 5} .0046669{col 66}{space 3} .0493495
{txt}{space 15}(2 vs 0)  2  {c |}{col 29}{res}{space 2} .0256752{col 41}{space 2} .0110209{col 52}{space 5} .0040747{col 66}{space 3} .0472757
{txt}{space 15}(2 vs 0)  3  {c |}{col 29}{res}{space 2}   .01976{col 41}{space 2}  .010728{col 52}{space 5}-.0012665{col 66}{space 3} .0407866
{txt}{space 15}(2 vs 0)  4  {c |}{col 29}{res}{space 2} .0142251{col 41}{space 2} .0126695{col 52}{space 5}-.0106067{col 66}{space 3} .0390568
{txt}{space 15}(2 vs 0)  5  {c |}{col 29}{res}{space 2} .0102615{col 41}{space 2} .0152414{col 52}{space 5}-.0196111{col 66}{space 3} .0401341
{txt}{space 15}(2 vs 0)  6  {c |}{col 29}{res}{space 2} .0077278{col 41}{space 2} .0175337{col 52}{space 5}-.0266377{col 66}{space 3} .0420932
{txt}{space 15}(2 vs 0)  7  {c |}{col 29}{res}{space 2}  .006482{col 41}{space 2} .0192832{col 52}{space 5}-.0313123{col 66}{space 3} .0442763
{txt}{space 15}(2 vs 0)  8  {c |}{col 29}{res}{space 2} .0063825{col 41}{space 2} .0204597{col 52}{space 5}-.0337177{col 66}{space 3} .0464827
{txt}{space 15}(2 vs 0)  9  {c |}{col 29}{res}{space 2} .0072875{col 41}{space 2} .0211254{col 52}{space 5}-.0341175{col 66}{space 3} .0486926
{txt}{space 15}(2 vs 0) 10  {c |}{col 29}{res}{space 2} .0090554{col 41}{space 2} .0213892{col 52}{space 5}-.0328667{col 66}{space 3} .0509776
{txt}{space 15}(2 vs 0) 11  {c |}{col 29}{res}{space 2} .0115444{col 41}{space 2} .0213908{col 52}{space 5}-.0303807{col 66}{space 3} .0534696
{txt}{space 15}(2 vs 0) 12  {c |}{col 29}{res}{space 2} .0146128{col 41}{space 2} .0212919{col 52}{space 5}-.0271186{col 66}{space 3} .0563443
{txt}{hline 28}{c BT}{hline 11}{hline 11}{hline 14}{hline 12}
{res}{txt}
{com}. 
. marginsplot, recast(connected) ciopt(color(%40)) recastci(rarea) /// 
> yline(0, lcolor(%40gs) lpattern(shortdash)) ///
> legend(on order(1 "High Task Complexity - Low Task Complexity") pos(6) ring(2) cols(2) size(10pt))  ///
> title(" {c -(}bf:FIGURE B2F{c )-}""{c -(}bf:Conditional Adaptation Marginal Effect By Task Complexity{c )-}" "{c -(}bf:(Seeking Different Occupation: Relative Type I Program Error Rate [MODEL B3]){c )-}", size(10pt) linegap(0.7) margin(t+1 b+1 r-6)) ///
> xtitle("Months since IT Reform", size(10pt) margin(t+2 b+2)) ///
> ytitle("Relative Type I Program Error Rate", size(10pt) margin(r+2)) ///
> xlabel(0 "0" 1 "1" 6 "6" 12 "12" 18 "18" 24 "24" 30 "30" 36 "36" 42 "42" 48 "48" 54 "54" 60 "60", labsize(9pt) ) ///
> ylabel(, labsize(9pt) format(%9.2f) angle(0)) xsize(6)
{res}
{text}{p 0 0 2}Variables that uniquely identify margins: {bf:itmod_monthcount}{p_end}
{p 0 4 2}
{txt}(note:  named style
% 40gs not found in class
color,  default attributes used)
{p_end}
{res}{txt}
{com}. *
. *
. *
. graph save "Graph" "C:\Users\gk57526\Dropbox\Administration of UI Programs in the American States (Ji-Hyeun Hong)\Performance Management Project (Paper #3)\EMPIRICS\GRAPHICS\marginsplot.Model B3.FIGURE B2F.04-10-2025.gph", replace
{txt}{p 0 4 2}
(file {bf}
C:\Users\gk57526\Dropbox\Administration of UI Programs in the American States (Ji-Hyeun Hong)\Performance Management Project (Paper #3)\EMPIRICS\GRAPHICS\marginsplot.Model B3.FIGURE B2F.04-10-2025.gph{rm}
not found)
{p_end}
{res}{txt}file {bf:C:\Users\gk57526\Dropbox\Administration of UI Programs in the American States (Ji-Hyeun Hong)\Performance Management Project (Paper #3)\EMPIRICS\GRAPHICS\marginsplot.Model B3.FIGURE B2F.04-10-2025.gph} saved

{com}. 
. 
. 
. 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**********************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************
. 
. 
. 
. log close
      {txt}name:  {res}<unnamed>
       {txt}log:  {res}C:\Users\gk57526\Dropbox\Administration of UI Programs in the American States (Ji-Hyeun Hong)\Performance Management Project (Paper #3)\EMPIRICS\OUTPUT\Performance Management.APPENDIX B MODELS.04-10-2025.smcl
  {txt}log type:  {res}smcl
 {txt}closed on:  {res}11 Apr 2025, 02:05:54
{txt}{.-}
{smcl}
{txt}{sf}{ul off}